01. Series, DataFrame and Reindex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e17893ad",
   "metadata": {},
   "source": [
    "## <font color=green> Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2c85443",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Comando para exibir todas as colunas do arquivo\n",
    "# pd.set_option('display.max_columns', None)\n",
    "# pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99b330c",
   "metadata": {},
   "source": [
    "- Series são objetos unidimensionais contendo dados e labels (ou index)\n",
    "- Formas de criação de Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc2b20ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    e\n",
       "5    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(list('abcdef'))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dabc6a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([2, 4, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f156f93",
   "metadata": {},
   "source": [
    "- O index pode ser especificado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5b469ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    2\n",
       "a    4\n",
       "c    6\n",
       "e    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([2, 4, 6, 8], index = ['f', 'a', 'c', 'e'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2578907",
   "metadata": {},
   "source": [
    "- O valor pode ser selecionado pelo seu index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6034a84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd13384",
   "metadata": {},
   "source": [
    "- Assim como múltiplos valores também podem ser selecionados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "051c5045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    4\n",
       "c    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[['a','c']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5146c0",
   "metadata": {},
   "source": [
    "- Os indices não precisam ser valores únicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "defd3c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "a    2\n",
       "b    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series(range(4), index = list('abab'))\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4052fbbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dda5ca0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['a'][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16979fa3",
   "metadata": {},
   "source": [
    "- Series suportam operações de filtragem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e434cad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    2\n",
       "a    4\n",
       "c    6\n",
       "e    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3d0c120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    6\n",
       "e    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc61953e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    False\n",
       "a    False\n",
       "c     True\n",
       "e     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s>4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "933470f6",
   "metadata": {},
   "source": [
    "- Podemos realizar também operações aritméticas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83b49e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f     6\n",
       "a     8\n",
       "c    10\n",
       "e    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s+4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd0b8174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f     8\n",
       "a    16\n",
       "c    24\n",
       "e    32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s*4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef7f131",
   "metadata": {},
   "source": [
    "- Series suportam variáveis nulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6456571a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    100\n",
       "c    150\n",
       "d    200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata = {'b': 100, 'c': 150, 'd': 200}\n",
    "s = pd.Series(sdata)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e655166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    100.0\n",
       "c    150.0\n",
       "d    200.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(sdata, list('abcd'))\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdb7b1c",
   "metadata": {},
   "source": [
    "- É possível realizar também operações aritméticas entre duas séries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3fb4156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    1\n",
       "b    2\n",
       "a    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([1,2,3], index = ['c','b','a'])\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "758877fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    200.0\n",
       "c    150.0\n",
       "d      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s*s2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9184beb2",
   "metadata": {},
   "source": [
    "## <font color=green> DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9d622f",
   "metadata": {},
   "source": [
    "- DataFrames são como planilhas, uma estrutura de dados contendo uma coleção de colunas.\n",
    "- Possui linhas e colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ba98050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pontuação</th>\n",
       "      <th>time</th>\n",
       "      <th>ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pontuação         time   ano\n",
       "0         81  Corinthians  2015\n",
       "1         80    Palmeiras  2016\n",
       "2         72  Corinthians  2017\n",
       "3         80    Palmeiras  2018\n",
       "4         90     Flamengo  2019\n",
       "5         71     Flamengo  2020"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'pontuação':[81,80,72,80,90,71],\n",
    "        'time':['Corinthians','Palmeiras','Corinthians','Palmeiras','Flamengo','Flamengo'],\n",
    "        'ano':[2015,2016,2017,2018,2019,2020]}\n",
    "frame = pd.DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1dee30b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corinthians</th>\n",
       "      <th>Palmeiras</th>\n",
       "      <th>Flamengo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Corinthians  Palmeiras  Flamengo\n",
       "2015         81.0        NaN       NaN\n",
       "2017         72.0        NaN       NaN\n",
       "2016          NaN       80.0       NaN\n",
       "2018          NaN       80.0       NaN\n",
       "2019          NaN        NaN      90.0\n",
       "2020          NaN        NaN      71.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_data = {'Corinthians': {2015:81, 2017:72},\n",
    "        'Palmeiras': {2016:80, 2018:80},\n",
    "        'Flamengo': {2019:90,2020:71}        \n",
    "        }\n",
    "        \n",
    "pop = pd.DataFrame(pop_data)\n",
    "pop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5c093f",
   "metadata": {},
   "source": [
    "- As colunas podem ser retornadas como uma série"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2ebce59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Corinthians\n",
       "1      Palmeiras\n",
       "2    Corinthians\n",
       "3      Palmeiras\n",
       "4       Flamengo\n",
       "5       Flamengo\n",
       "Name: time, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['time']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8757ae31",
   "metadata": {},
   "source": [
    "- O atributo values retorna os dados contidos no DataFrame como um array bidimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "258f7e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[81., nan, nan],\n",
       "       [72., nan, nan],\n",
       "       [nan, 80., nan],\n",
       "       [nan, 80., nan],\n",
       "       [nan, nan, 90.],\n",
       "       [nan, nan, 71.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7003bfde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, 90., 71.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop['Flamengo'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4bc00f",
   "metadata": {},
   "source": [
    "- Novas linhas podem ser adicionadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04ec5f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pontuação</th>\n",
       "      <th>time</th>\n",
       "      <th>ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>84</td>\n",
       "      <td>Atlético Mineiro</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pontuação              time   ano\n",
       "0         81       Corinthians  2015\n",
       "1         80         Palmeiras  2016\n",
       "2         72       Corinthians  2017\n",
       "3         80         Palmeiras  2018\n",
       "4         90          Flamengo  2019\n",
       "5         71          Flamengo  2020\n",
       "6         84  Atlético Mineiro  2021"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_line = {'pontuação':84,'time':'Atlético Mineiro','ano':2021}\n",
    "frame = frame.append(new_line, ignore_index=True)\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e126bc",
   "metadata": {},
   "source": [
    "- Novas colunas podem ser adicionadas (por cálculo aritmético ou atribuição direta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c04426c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe816acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pontuação</th>\n",
       "      <th>time</th>\n",
       "      <th>ano</th>\n",
       "      <th>nova coluna</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>84</td>\n",
       "      <td>Atlético Mineiro</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pontuação              time   ano  nova coluna\n",
       "0         81       Corinthians  2015          NaN\n",
       "1         80         Palmeiras  2016          NaN\n",
       "2         72       Corinthians  2017          NaN\n",
       "3         80         Palmeiras  2018          NaN\n",
       "4         90          Flamengo  2019          NaN\n",
       "5         71          Flamengo  2020          NaN\n",
       "6         84  Atlético Mineiro  2021          NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['nova coluna'] = np.NaN\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0f6036f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pontuação</th>\n",
       "      <th>time</th>\n",
       "      <th>ano</th>\n",
       "      <th>nova coluna</th>\n",
       "      <th>processada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>84</td>\n",
       "      <td>Atlético Mineiro</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pontuação              time   ano  nova coluna  processada\n",
       "0         81       Corinthians  2015          NaN         162\n",
       "1         80         Palmeiras  2016          NaN         160\n",
       "2         72       Corinthians  2017          NaN         144\n",
       "3         80         Palmeiras  2018          NaN         160\n",
       "4         90          Flamengo  2019          NaN         180\n",
       "5         71          Flamengo  2020          NaN         142\n",
       "6         84  Atlético Mineiro  2021          NaN         168"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['processada'] = frame['pontuação'] * 2\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1283f70",
   "metadata": {},
   "source": [
    "- DataFrames permitem trocar linhas por colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01ec2d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2015</th>\n",
       "      <th>2017</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corinthians</th>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palmeiras</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flamengo</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             2015  2017  2016  2018  2019  2020\n",
       "Corinthians  81.0  72.0   NaN   NaN   NaN   NaN\n",
       "Palmeiras     NaN   NaN  80.0  80.0   NaN   NaN\n",
       "Flamengo      NaN   NaN   NaN   NaN  90.0  71.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3b499e",
   "metadata": {},
   "source": [
    "- DataFrame tem suporte a funções descritivas e estatísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48f59d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corinthians</th>\n",
       "      <th>Palmeiras</th>\n",
       "      <th>Flamengo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Corinthians  Palmeiras  Flamengo\n",
       "2015         81.0        NaN       NaN\n",
       "2017         72.0        NaN       NaN\n",
       "2016          NaN       80.0       NaN\n",
       "2018          NaN       80.0       NaN\n",
       "2019          NaN        NaN      90.0\n",
       "2020          NaN        NaN      71.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f93989b",
   "metadata": {},
   "source": [
    "- Describe gera vários dados estatísticos de resumo de uma só vez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4da35b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Corinthians</th>\n",
       "      <th>Palmeiras</th>\n",
       "      <th>Flamengo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>76.500000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.363961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.435029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.250000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>75.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.500000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.750000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Corinthians  Palmeiras   Flamengo\n",
       "count     2.000000        2.0   2.000000\n",
       "mean     76.500000       80.0  80.500000\n",
       "std       6.363961        0.0  13.435029\n",
       "min      72.000000       80.0  71.000000\n",
       "25%      74.250000       80.0  75.750000\n",
       "50%      76.500000       80.0  80.500000\n",
       "75%      78.750000       80.0  85.250000\n",
       "max      81.000000       80.0  90.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f815d2d",
   "metadata": {},
   "source": [
    "- Info retorna o número de valores não nulos e o tipo por coluna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a15b98c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6 entries, 2015 to 2020\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Corinthians  2 non-null      float64\n",
      " 1   Palmeiras    2 non-null      float64\n",
      " 2   Flamengo     2 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 364.0 bytes\n"
     ]
    }
   ],
   "source": [
    "pop.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b56fa3d",
   "metadata": {},
   "source": [
    "- Chamar o método sum de DataFrame devolve uma Series contendo as soma das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "caa82032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corinthians    153.0\n",
       "Palmeiras      160.0\n",
       "Flamengo       161.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f576ab",
   "metadata": {},
   "source": [
    "- Passar axis='columns' faz a soma pelas colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d4d8f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    81.0\n",
       "2017    72.0\n",
       "2016    80.0\n",
       "2018    80.0\n",
       "2019    90.0\n",
       "2020    71.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.sum(axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02255ff",
   "metadata": {},
   "source": [
    "- Mean retorna a média dos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf38fc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corinthians    76.5\n",
       "Palmeiras      80.0\n",
       "Flamengo       80.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa9ec91",
   "metadata": {},
   "source": [
    "- Median retorna a mediana dos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f322ae30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corinthians    76.5\n",
       "Palmeiras      80.0\n",
       "Flamengo       80.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347184fd",
   "metadata": {},
   "source": [
    "- min e max calcula os valores mínimo e máximo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "317cbd46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corinthians    72.0\n",
       "Palmeiras      80.0\n",
       "Flamengo       71.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fad67a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Corinthians    81.0\n",
       "Palmeiras      80.0\n",
       "Flamengo       90.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ce7a31",
   "metadata": {},
   "source": [
    "- cumsum calcula a soma cumulativa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "93385225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-02</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-03</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-04</td>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-05</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-08-06</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Total Sales\n",
       "0  2022-08-01         2000\n",
       "1  2022-08-02         3000\n",
       "2  2022-08-03         2500\n",
       "3  2022-08-04         4000\n",
       "4  2022-08-05         3000\n",
       "5  2022-08-06         2000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.DataFrame({'Date':['2022-08-01','2022-08-02','2022-08-03','2022-08-04','2022-08-05','2022-08-06'],\n",
    "                   'Total Sales':[2000,3000,2500,4000,3000,2000]\n",
    "                   })\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2f40984d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Total Sales</th>\n",
       "      <th>Cummulative Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-02</td>\n",
       "      <td>3000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-03</td>\n",
       "      <td>2500</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-04</td>\n",
       "      <td>4000</td>\n",
       "      <td>11500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-05</td>\n",
       "      <td>3000</td>\n",
       "      <td>14500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-08-06</td>\n",
       "      <td>2000</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Total Sales  Cummulative Sales\n",
       "0  2022-08-01         2000               2000\n",
       "1  2022-08-02         3000               5000\n",
       "2  2022-08-03         2500               7500\n",
       "3  2022-08-04         4000              11500\n",
       "4  2022-08-05         3000              14500\n",
       "5  2022-08-06         2000              16500"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Cummulative Sales'] = sales['Total Sales'].cumsum()\n",
    "sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c77284e",
   "metadata": {},
   "source": [
    "- melt transforma um DataFrame de várias colunas para várias linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "55e58763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>Aldi</th>\n",
       "      <th>Costco</th>\n",
       "      <th>Target</th>\n",
       "      <th>Walmart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>banana</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orange</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit  Aldi  Costco  Target  Walmart\n",
       "0   apple     4       1       3        6\n",
       "1  banana     5       2       4        7\n",
       "2  orange     6       3       5        8"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fruits = pd.DataFrame({\n",
    "    \"fruit\": [\"apple\", \"banana\", \"orange\"],\n",
    "    \"Aldi\": [4, 5, 6],\n",
    "    \"Costco\": [1, 2, 3],\n",
    "    \"Target\": [3, 4, 5],\n",
    "    \"Walmart\": [6, 7, 8]\n",
    "})\n",
    "\n",
    "df_fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2858cce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>store</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>banana</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orange</td>\n",
       "      <td>Aldi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apple</td>\n",
       "      <td>Costco</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>banana</td>\n",
       "      <td>Costco</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orange</td>\n",
       "      <td>Costco</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>apple</td>\n",
       "      <td>Target</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>banana</td>\n",
       "      <td>Target</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>orange</td>\n",
       "      <td>Target</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apple</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>banana</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>orange</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     fruit    store  value\n",
       "0    apple     Aldi      4\n",
       "1   banana     Aldi      5\n",
       "2   orange     Aldi      6\n",
       "3    apple   Costco      1\n",
       "4   banana   Costco      2\n",
       "5   orange   Costco      3\n",
       "6    apple   Target      3\n",
       "7   banana   Target      4\n",
       "8   orange   Target      5\n",
       "9    apple  Walmart      6\n",
       "10  banana  Walmart      7\n",
       "11  orange  Walmart      8"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fruits.melt(id_vars=[\"fruit\"],\n",
    "        value_vars=[\"Aldi\", \"Costco\", \"Target\", \"Walmart\"],\n",
    "        var_name='store')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a07040",
   "metadata": {},
   "source": [
    "- Pandas carrega vários tipos de dados como csv, json, xml, html, excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aa6240f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>05-02-2010</td>\n",
       "      <td>1643690.90</td>\n",
       "      <td>0</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12-02-2010</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>1</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>19-02-2010</td>\n",
       "      <td>1611968.17</td>\n",
       "      <td>0</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26-02-2010</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>0</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>05-03-2010</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>45</td>\n",
       "      <td>28-09-2012</td>\n",
       "      <td>713173.95</td>\n",
       "      <td>0</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6431</th>\n",
       "      <td>45</td>\n",
       "      <td>05-10-2012</td>\n",
       "      <td>733455.07</td>\n",
       "      <td>0</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>12-10-2012</td>\n",
       "      <td>734464.36</td>\n",
       "      <td>0</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>45</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>718125.53</td>\n",
       "      <td>0</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>45</td>\n",
       "      <td>26-10-2012</td>\n",
       "      <td>760281.43</td>\n",
       "      <td>0</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0         1  05-02-2010    1643690.90             0        42.31       2.572   \n",
       "1         1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "2         1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "3         1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "4         1  05-03-2010    1554806.68             0        46.50       2.625   \n",
       "...     ...         ...           ...           ...          ...         ...   \n",
       "6430     45  28-09-2012     713173.95             0        64.88       3.997   \n",
       "6431     45  05-10-2012     733455.07             0        64.89       3.985   \n",
       "6432     45  12-10-2012     734464.36             0        54.47       4.000   \n",
       "6433     45  19-10-2012     718125.53             0        56.47       3.969   \n",
       "6434     45  26-10-2012     760281.43             0        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
       "...          ...           ...  \n",
       "6430  192.013558         8.684  \n",
       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart = pd.read_csv('datasets/Walmart_Store_sales.csv')\n",
    "walmart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25371d9e",
   "metadata": {},
   "source": [
    "- É possível filtrar as colunas ao carregar o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e97bf526",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>6435 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "      Store        Date  Weekly_Sales\n",
       "0         1  05-02-2010    1643690.90\n",
       "1         1  12-02-2010    1641957.44\n",
       "2         1  19-02-2010    1611968.17\n",
       "3         1  26-02-2010    1409727.59\n",
       "4         1  05-03-2010    1554806.68\n",
       "...     ...         ...           ...\n",
       "6430     45  28-09-2012     713173.95\n",
       "6431     45  05-10-2012     733455.07\n",
       "6432     45  12-10-2012     734464.36\n",
       "6433     45  19-10-2012     718125.53\n",
       "6434     45  26-10-2012     760281.43\n",
       "\n",
       "[6435 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", usecols=[\"Store\", \"Date\", \"Weekly_Sales\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa355d3",
   "metadata": {},
   "source": [
    "- É possível especificar o tipo da coluna ao carregar o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b17f2e3",
   "metadata": {},
   "outputs": [
    {
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       "<p>6435 rows × 8 columns</p>\n",
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      "text/plain": [
       "     Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1  05-02-2010    1643690.90             0        42.31       2.572   \n",
       "1        1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "2        1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "3        1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "4        1  05-03-2010    1554806.68             0        46.50       2.625   \n",
       "...    ...         ...           ...           ...          ...         ...   \n",
       "6430    45  28-09-2012     713173.95             0        64.88       3.997   \n",
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       "6433    45  19-10-2012     718125.53             0        56.47       3.969   \n",
       "6434    45  26-10-2012     760281.43             0        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
       "...          ...           ...  \n",
       "6430  192.013558         8.684  \n",
       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", dtype={\"Store\": \"category\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8ebef9",
   "metadata": {},
   "source": [
    "- É possível especificar uma coluna como data ao carregar o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8a8aab28",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0         1 2010-05-02    1643690.90             0        42.31       2.572   \n",
       "1         1 2010-12-02    1641957.44             1        38.51       2.548   \n",
       "2         1 2010-02-19    1611968.17             0        39.93       2.514   \n",
       "3         1 2010-02-26    1409727.59             0        46.63       2.561   \n",
       "4         1 2010-05-03    1554806.68             0        46.50       2.625   \n",
       "...     ...        ...           ...           ...          ...         ...   \n",
       "6430     45 2012-09-28     713173.95             0        64.88       3.997   \n",
       "6431     45 2012-05-10     733455.07             0        64.89       3.985   \n",
       "6432     45 2012-12-10     734464.36             0        54.47       4.000   \n",
       "6433     45 2012-10-19     718125.53             0        56.47       3.969   \n",
       "6434     45 2012-10-26     760281.43             0        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
       "...          ...           ...  \n",
       "6430  192.013558         8.684  \n",
       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", parse_dates=[\"Date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e55b6da",
   "metadata": {},
   "source": [
    "- Especificar uma coluna como index ao carregar o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7ddb1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
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       "  <tbody>\n",
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       "      <th>05-02-2010</th>\n",
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       "    <tr>\n",
       "      <th>19-02-2010</th>\n",
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       "      <td>1611968.17</td>\n",
       "      <td>0</td>\n",
       "      <td>39.93</td>\n",
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       "      <td>211.289143</td>\n",
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       "    <tr>\n",
       "      <th>26-02-2010</th>\n",
       "      <td>1</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>0</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-2010</th>\n",
       "      <td>1</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
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       "      <th>28-09-2012</th>\n",
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       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
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       "    <tr>\n",
       "      <th>05-10-2012</th>\n",
       "      <td>45</td>\n",
       "      <td>733455.07</td>\n",
       "      <td>0</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
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       "    <tr>\n",
       "      <th>12-10-2012</th>\n",
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       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
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       "</table>\n",
       "<p>6435 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "Date                                                                     \n",
       "05-02-2010      1    1643690.90             0        42.31       2.572   \n",
       "12-02-2010      1    1641957.44             1        38.51       2.548   \n",
       "19-02-2010      1    1611968.17             0        39.93       2.514   \n",
       "26-02-2010      1    1409727.59             0        46.63       2.561   \n",
       "05-03-2010      1    1554806.68             0        46.50       2.625   \n",
       "...           ...           ...           ...          ...         ...   \n",
       "28-09-2012     45     713173.95             0        64.88       3.997   \n",
       "05-10-2012     45     733455.07             0        64.89       3.985   \n",
       "12-10-2012     45     734464.36             0        54.47       4.000   \n",
       "19-10-2012     45     718125.53             0        56.47       3.969   \n",
       "26-10-2012     45     760281.43             0        58.85       3.882   \n",
       "\n",
       "                   CPI  Unemployment  \n",
       "Date                                  \n",
       "05-02-2010  211.096358         8.106  \n",
       "12-02-2010  211.242170         8.106  \n",
       "19-02-2010  211.289143         8.106  \n",
       "26-02-2010  211.319643         8.106  \n",
       "05-03-2010  211.350143         8.106  \n",
       "...                ...           ...  \n",
       "28-09-2012  192.013558         8.684  \n",
       "05-10-2012  192.170412         8.667  \n",
       "12-10-2012  192.327265         8.667  \n",
       "19-10-2012  192.330854         8.667  \n",
       "26-10-2012  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", index_col=[\"Date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89e93b5",
   "metadata": {},
   "source": [
    "- Especificar o número de linhas a serem carregadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e92beead",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0      1  05-02-2010    1643690.90             0        42.31       2.572   \n",
       "1      1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "2      1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "3      1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "4      1  05-03-2010    1554806.68             0        46.50       2.625   \n",
       "\n",
       "          CPI  Unemployment  \n",
       "0  211.096358         8.106  \n",
       "1  211.242170         8.106  \n",
       "2  211.289143         8.106  \n",
       "3  211.319643         8.106  \n",
       "4  211.350143         8.106  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", nrows=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c6b94d",
   "metadata": {},
   "source": [
    "- Especificar linhas a não serem carregadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "023c703c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1394393.84</td>\n",
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       "      <td>192.013558</td>\n",
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       "    <tr>\n",
       "      <th>6331</th>\n",
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       "      <td>733455.07</td>\n",
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       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
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       "    <tr>\n",
       "      <th>6332</th>\n",
       "      <td>45</td>\n",
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       "      <td>0</td>\n",
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       "      <td>56.47</td>\n",
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       "    <tr>\n",
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       "      <td>45</td>\n",
       "      <td>26-10-2012</td>\n",
       "      <td>760281.43</td>\n",
       "      <td>0</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6335 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1  30-12-2011  1497462.72  1.1  44.55  3.129  219.5359898  7.866\n",
       "0      1  06-01-2012  1550369.92    0  49.01  3.157   219.714258  7.348\n",
       "1      1  13-01-2012  1459601.17    0  48.53  3.261   219.892526  7.348\n",
       "2      1  20-01-2012  1394393.84    0  54.11  3.268   219.985689  7.348\n",
       "3      1  27-01-2012  1319325.59    0  54.26  3.290   220.078852  7.348\n",
       "4      1  03-02-2012  1636339.65    0  56.55  3.360   220.172015  7.348\n",
       "...   ..         ...         ...  ...    ...    ...          ...    ...\n",
       "6330  45  28-09-2012   713173.95    0  64.88  3.997   192.013558  8.684\n",
       "6331  45  05-10-2012   733455.07    0  64.89  3.985   192.170412  8.667\n",
       "6332  45  12-10-2012   734464.36    0  54.47  4.000   192.327265  8.667\n",
       "6333  45  19-10-2012   718125.53    0  56.47  3.969   192.330854  8.667\n",
       "6334  45  26-10-2012   760281.43    0  58.85  3.882   192.308899  8.667\n",
       "\n",
       "[6335 rows x 8 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", skiprows=100)  # skips the first 100 lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "40493928",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0         1  12-02-2010    1641957.44             1        38.51       2.548   \n",
       "1         1  19-02-2010    1611968.17             0        39.93       2.514   \n",
       "2         1  26-02-2010    1409727.59             0        46.63       2.561   \n",
       "3         1  12-03-2010    1439541.59             0        57.79       2.667   \n",
       "4         1  19-03-2010    1472515.79             0        54.58       2.720   \n",
       "...     ...         ...           ...           ...          ...         ...   \n",
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       "6432     45  26-10-2012     760281.43             0        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.242170         8.106  \n",
       "1     211.289143         8.106  \n",
       "2     211.319643         8.106  \n",
       "3     211.380643         8.106  \n",
       "4     211.215635         8.106  \n",
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       "\n",
       "[6433 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/Walmart_Store_sales.csv\", skiprows=[1, 5])  # skips line 1 and 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d07202",
   "metadata": {},
   "source": [
    "- É possível alterar os tipos das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "471d8a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6435 entries, 0 to 6434\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Store         6435 non-null   int64  \n",
      " 1   Date          6435 non-null   object \n",
      " 2   Weekly_Sales  6435 non-null   float64\n",
      " 3   Holiday_Flag  6435 non-null   int64  \n",
      " 4   Temperature   6435 non-null   float64\n",
      " 5   Fuel_Price    6435 non-null   float64\n",
      " 6   CPI           6435 non-null   float64\n",
      " 7   Unemployment  6435 non-null   float64\n",
      "dtypes: float64(5), int64(2), object(1)\n",
      "memory usage: 402.3+ KB\n"
     ]
    }
   ],
   "source": [
    "walmart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4e7b1f8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6435 entries, 0 to 6434\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Store         6435 non-null   category      \n",
      " 1   Date          6435 non-null   datetime64[ns]\n",
      " 2   Weekly_Sales  6435 non-null   float64       \n",
      " 3   Holiday_Flag  6435 non-null   bool          \n",
      " 4   Temperature   6435 non-null   float64       \n",
      " 5   Fuel_Price    6435 non-null   float64       \n",
      " 6   CPI           6435 non-null   float64       \n",
      " 7   Unemployment  6435 non-null   float64       \n",
      "dtypes: bool(1), category(1), datetime64[ns](1), float64(5)\n",
      "memory usage: 315.7 KB\n"
     ]
    }
   ],
   "source": [
    "walmart = walmart.astype(\n",
    "    {\n",
    "        \"Date\": \"datetime64[ns]\",\n",
    "        \"Holiday_Flag\": \"bool\",\n",
    "        \"Store\": \"category\",\n",
    "    }\n",
    ")\n",
    "walmart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8dfdf193",
   "metadata": {},
   "outputs": [
    {
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       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
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       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
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       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d8664c",
   "metadata": {},
   "source": [
    "- É possível realizar consultas por coluna e por index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "33035bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6375</th>\n",
       "      <td>45</td>\n",
       "      <td>2011-09-09</td>\n",
       "      <td>746129.56</td>\n",
       "      <td>True</td>\n",
       "      <td>71.48</td>\n",
       "      <td>3.738</td>\n",
       "      <td>186.673738</td>\n",
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       "    <tr>\n",
       "      <th>6386</th>\n",
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       "      <td>2011-11-25</td>\n",
       "      <td>1170672.94</td>\n",
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       "      <td>48.71</td>\n",
       "      <td>3.492</td>\n",
       "      <td>188.350400</td>\n",
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       "      <td>3.911</td>\n",
       "      <td>191.577676</td>\n",
       "      <td>8.684</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "31       1 2010-10-09    1507460.69          True        78.69       2.565   \n",
       "42       1 2010-11-26    1955624.11          True        64.52       2.735   \n",
       "47       1 2010-12-31    1367320.01          True        48.43       2.943   \n",
       "53       1 2011-11-02    1649614.93          True        36.39       3.022   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6375    45 2011-09-09     746129.56          True        71.48       3.738   \n",
       "6386    45 2011-11-25    1170672.94          True        48.71       3.492   \n",
       "6391    45 2011-12-30     869403.63          True        37.79       3.389   \n",
       "6397    45 2012-10-02     803657.12          True        37.00       3.640   \n",
       "6427    45 2012-07-09     766512.66          True        75.70       3.911   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "1     211.242170         8.106  \n",
       "31    211.495190         7.787  \n",
       "42    211.748433         7.838  \n",
       "47    211.404932         7.838  \n",
       "53    212.936705         7.742  \n",
       "...          ...           ...  \n",
       "6375  186.673738         8.625  \n",
       "6386  188.350400         8.523  \n",
       "6391  189.062016         8.523  \n",
       "6397  189.707605         8.424  \n",
       "6427  191.577676         8.684  \n",
       "\n",
       "[450 rows x 8 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query('Holiday_Flag == 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8ae5aecb",
   "metadata": {},
   "outputs": [
    {
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       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "189      2 2010-12-24    3436007.68         False        49.97       2.886   \n",
       "241      2 2011-12-23    3224369.80         False        46.66       3.112   \n",
       "475      4 2010-12-24    3526713.39         False        43.21       2.887   \n",
       "527      4 2011-12-23    3676388.98         False        35.92       3.103   \n",
       "1333    10 2010-12-24    3749057.69         False        57.06       3.236   \n",
       "1385    10 2011-12-23    3487986.89         False        48.36       3.541   \n",
       "1762    13 2010-12-24    3595903.20         False        34.90       2.846   \n",
       "1814    13 2011-12-23    3556766.03         False        24.76       3.186   \n",
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       "1957    14 2011-12-23    3369068.99         False        42.27       3.389   \n",
       "2763    20 2010-12-24    3766687.43         False        25.17       3.141   \n",
       "2815    20 2011-12-23    3555371.03         False        40.19       3.389   \n",
       "3764    27 2010-12-24    3078162.08         False        31.34       3.309   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "189   211.064660         8.163  \n",
       "241   218.999550         7.441  \n",
       "475   126.983581         7.127  \n",
       "527   129.984548         5.143  \n",
       "1333  126.983581         9.003  \n",
       "1385  129.984548         7.874  \n",
       "1762  126.983581         7.795  \n",
       "1814  129.984548         6.392  \n",
       "1905  182.544590         8.724  \n",
       "1957  188.929975         8.523  \n",
       "2763  204.637673         7.484  \n",
       "2815  212.236040         7.082  \n",
       "3764  136.597273         8.021  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = 3049614.93\n",
    "walmart.query('Weekly_Sales > @val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "933d923d",
   "metadata": {},
   "outputs": [
    {
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       "  Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "1     1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2     1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3     1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4     1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "5     1 2010-12-03    1439541.59         False        57.79       2.667   \n",
       "6     1 2010-03-19    1472515.79         False        54.58       2.720   \n",
       "\n",
       "          CPI  Unemployment  \n",
       "1  211.242170         8.106  \n",
       "2  211.289143         8.106  \n",
       "3  211.319643         8.106  \n",
       "4  211.350143         8.106  \n",
       "5  211.380643         8.106  \n",
       "6  211.215635         8.106  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query('1 <= index < 7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b103a0ea",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-05-03</td>\n",
       "      <td>1554806.68</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
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       "      <td>2012-09-28</td>\n",
       "      <td>1746470.56</td>\n",
       "      <td>False</td>\n",
       "      <td>79.45</td>\n",
       "      <td>3.666</td>\n",
       "      <td>222.616433</td>\n",
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       "      <th>282</th>\n",
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       "      <td>1998321.04</td>\n",
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       "      <td>3.617</td>\n",
       "      <td>222.815930</td>\n",
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       "      <td>3.594</td>\n",
       "      <td>223.059808</td>\n",
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       "      <td>2012-10-26</td>\n",
       "      <td>1834458.35</td>\n",
       "      <td>False</td>\n",
       "      <td>69.79</td>\n",
       "      <td>3.506</td>\n",
       "      <td>223.078337</td>\n",
       "      <td>6.170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>286 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0       1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1       1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2       1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3       1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4       1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "..    ...        ...           ...           ...          ...         ...   \n",
       "281     2 2012-09-28    1746470.56         False        79.45       3.666   \n",
       "282     2 2012-05-10    1998321.04         False        70.27       3.617   \n",
       "283     2 2012-12-10    1900745.13         False        60.97       3.601   \n",
       "284     2 2012-10-19    1847990.41         False        68.08       3.594   \n",
       "285     2 2012-10-26    1834458.35         False        69.79       3.506   \n",
       "\n",
       "            CPI  Unemployment  \n",
       "0    211.096358         8.106  \n",
       "1    211.242170         8.106  \n",
       "2    211.289143         8.106  \n",
       "3    211.319643         8.106  \n",
       "4    211.350143         8.106  \n",
       "..          ...           ...  \n",
       "281  222.616433         6.565  \n",
       "282  222.815930         6.170  \n",
       "283  223.015426         6.170  \n",
       "284  223.059808         6.170  \n",
       "285  223.078337         6.170  \n",
       "\n",
       "[286 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Store in [1,2]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a6b88c9c",
   "metadata": {},
   "outputs": [
    {
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       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "573      5 2010-12-02     311825.70          True        39.81       2.548   \n",
       "1002     8 2010-12-02     994801.40          True        33.34       2.548   \n",
       "1145     9 2010-12-02     552677.48          True        37.08       2.548   \n",
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       "5721    41 2010-12-02    1075656.34          True        23.04       2.572   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "573   211.800470         6.566  \n",
       "1002  214.621419         6.299  \n",
       "1145  214.805653         6.415  \n",
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      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Date == '2010-12-02' and Temperature < 40 and Unemployment < 8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5acf30c3",
   "metadata": {},
   "outputs": [
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       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "8        1 2010-02-04    1594968.28         False        62.27       2.719   \n",
       "21       1 2010-02-07    1492418.14         False        80.91       2.669   \n",
       "145      2 2010-02-19    2124451.54         False        39.69       2.514   \n",
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       "6313    45 2010-02-07     800147.84         False        76.61       2.815   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "8     210.820450         7.808  \n",
       "21    211.223533         7.787  \n",
       "145   210.945160         8.324  \n",
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       "6294  182.034782         8.992  \n",
       "6295  182.077469         8.992  \n",
       "6300  181.865754         8.899  \n",
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       "\n",
       "[180 rows x 8 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Date.dt.month == 2 and Date.dt.year == 2010\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0edea25a",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2049860.26</td>\n",
       "      <td>False</td>\n",
       "      <td>36.45</td>\n",
       "      <td>2.540</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>8.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>5</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>303447.57</td>\n",
       "      <td>False</td>\n",
       "      <td>41.14</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.847128</td>\n",
       "      <td>6.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>6</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1567138.07</td>\n",
       "      <td>False</td>\n",
       "      <td>43.58</td>\n",
       "      <td>2.514</td>\n",
       "      <td>212.816155</td>\n",
       "      <td>7.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>7</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>506760.54</td>\n",
       "      <td>False</td>\n",
       "      <td>27.28</td>\n",
       "      <td>2.550</td>\n",
       "      <td>189.534100</td>\n",
       "      <td>9.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>8</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>963960.37</td>\n",
       "      <td>False</td>\n",
       "      <td>39.10</td>\n",
       "      <td>2.514</td>\n",
       "      <td>214.666488</td>\n",
       "      <td>6.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>9</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>511327.90</td>\n",
       "      <td>False</td>\n",
       "      <td>43.06</td>\n",
       "      <td>2.514</td>\n",
       "      <td>214.850618</td>\n",
       "      <td>6.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>10</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2113432.58</td>\n",
       "      <td>False</td>\n",
       "      <td>58.22</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>9.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>11</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1503298.70</td>\n",
       "      <td>False</td>\n",
       "      <td>48.30</td>\n",
       "      <td>2.514</td>\n",
       "      <td>214.619887</td>\n",
       "      <td>7.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>12</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1095421.65</td>\n",
       "      <td>False</td>\n",
       "      <td>54.83</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>13.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>13</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1970274.64</td>\n",
       "      <td>False</td>\n",
       "      <td>35.70</td>\n",
       "      <td>2.654</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>8.316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>14</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2204556.70</td>\n",
       "      <td>False</td>\n",
       "      <td>31.27</td>\n",
       "      <td>2.745</td>\n",
       "      <td>182.034782</td>\n",
       "      <td>8.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>15</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>660838.75</td>\n",
       "      <td>False</td>\n",
       "      <td>27.54</td>\n",
       "      <td>2.909</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>8.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2147</th>\n",
       "      <td>16</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>469868.70</td>\n",
       "      <td>False</td>\n",
       "      <td>21.13</td>\n",
       "      <td>2.550</td>\n",
       "      <td>189.534100</td>\n",
       "      <td>7.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>17</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>800714.00</td>\n",
       "      <td>False</td>\n",
       "      <td>25.06</td>\n",
       "      <td>2.654</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>6.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>18</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1150663.42</td>\n",
       "      <td>False</td>\n",
       "      <td>30.91</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>9.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>19</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1515976.11</td>\n",
       "      <td>False</td>\n",
       "      <td>28.57</td>\n",
       "      <td>2.909</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>8.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2719</th>\n",
       "      <td>20</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2161549.76</td>\n",
       "      <td>False</td>\n",
       "      <td>25.43</td>\n",
       "      <td>2.745</td>\n",
       "      <td>204.432100</td>\n",
       "      <td>8.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>21</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>867283.25</td>\n",
       "      <td>False</td>\n",
       "      <td>39.75</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.945160</td>\n",
       "      <td>8.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>22</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>988467.61</td>\n",
       "      <td>False</td>\n",
       "      <td>31.96</td>\n",
       "      <td>2.747</td>\n",
       "      <td>135.465778</td>\n",
       "      <td>8.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3148</th>\n",
       "      <td>23</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1319588.04</td>\n",
       "      <td>False</td>\n",
       "      <td>26.70</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>5.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3291</th>\n",
       "      <td>24</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1385362.49</td>\n",
       "      <td>False</td>\n",
       "      <td>31.05</td>\n",
       "      <td>2.909</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>8.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>25</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>676260.67</td>\n",
       "      <td>False</td>\n",
       "      <td>24.16</td>\n",
       "      <td>2.745</td>\n",
       "      <td>204.432100</td>\n",
       "      <td>8.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>26</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>999348.55</td>\n",
       "      <td>False</td>\n",
       "      <td>22.62</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>8.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3720</th>\n",
       "      <td>27</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1945070.33</td>\n",
       "      <td>False</td>\n",
       "      <td>32.44</td>\n",
       "      <td>2.909</td>\n",
       "      <td>135.465778</td>\n",
       "      <td>8.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>28</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1491300.42</td>\n",
       "      <td>False</td>\n",
       "      <td>54.83</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>13.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>29</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>542399.07</td>\n",
       "      <td>False</td>\n",
       "      <td>31.96</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>10.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4149</th>\n",
       "      <td>30</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>463513.26</td>\n",
       "      <td>False</td>\n",
       "      <td>39.75</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.945160</td>\n",
       "      <td>8.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4292</th>\n",
       "      <td>31</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1473386.75</td>\n",
       "      <td>False</td>\n",
       "      <td>39.75</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.945160</td>\n",
       "      <td>8.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4435</th>\n",
       "      <td>32</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1082559.06</td>\n",
       "      <td>False</td>\n",
       "      <td>29.16</td>\n",
       "      <td>2.550</td>\n",
       "      <td>189.534100</td>\n",
       "      <td>9.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>33</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>296850.83</td>\n",
       "      <td>False</td>\n",
       "      <td>62.16</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>10.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4721</th>\n",
       "      <td>34</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>983963.07</td>\n",
       "      <td>False</td>\n",
       "      <td>38.36</td>\n",
       "      <td>2.540</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>9.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4864</th>\n",
       "      <td>35</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1270658.64</td>\n",
       "      <td>False</td>\n",
       "      <td>32.44</td>\n",
       "      <td>2.745</td>\n",
       "      <td>135.465778</td>\n",
       "      <td>9.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>36</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>470281.03</td>\n",
       "      <td>False</td>\n",
       "      <td>45.66</td>\n",
       "      <td>2.472</td>\n",
       "      <td>210.045102</td>\n",
       "      <td>8.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5150</th>\n",
       "      <td>37</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>510382.50</td>\n",
       "      <td>False</td>\n",
       "      <td>45.66</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.045102</td>\n",
       "      <td>8.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5293</th>\n",
       "      <td>38</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>327237.92</td>\n",
       "      <td>False</td>\n",
       "      <td>54.83</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>13.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5436</th>\n",
       "      <td>39</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1230591.97</td>\n",
       "      <td>False</td>\n",
       "      <td>43.96</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.045102</td>\n",
       "      <td>8.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5579</th>\n",
       "      <td>40</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>916289.20</td>\n",
       "      <td>False</td>\n",
       "      <td>27.84</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>5.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5722</th>\n",
       "      <td>41</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>1052034.74</td>\n",
       "      <td>False</td>\n",
       "      <td>24.13</td>\n",
       "      <td>2.550</td>\n",
       "      <td>189.534100</td>\n",
       "      <td>7.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865</th>\n",
       "      <td>42</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>508794.87</td>\n",
       "      <td>False</td>\n",
       "      <td>58.22</td>\n",
       "      <td>2.915</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>9.765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6008</th>\n",
       "      <td>43</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>658997.55</td>\n",
       "      <td>False</td>\n",
       "      <td>48.77</td>\n",
       "      <td>2.514</td>\n",
       "      <td>203.247980</td>\n",
       "      <td>9.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>44</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>267956.30</td>\n",
       "      <td>False</td>\n",
       "      <td>35.70</td>\n",
       "      <td>2.654</td>\n",
       "      <td>126.526286</td>\n",
       "      <td>8.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6294</th>\n",
       "      <td>45</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>841264.04</td>\n",
       "      <td>False</td>\n",
       "      <td>31.27</td>\n",
       "      <td>2.745</td>\n",
       "      <td>182.034782</td>\n",
       "      <td>8.992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "145      2 2010-02-19    2124451.54         False        39.69       2.514   \n",
       "288      3 2010-02-19     421642.19         False        47.07       2.514   \n",
       "431      4 2010-02-19    2049860.26         False        36.45       2.540   \n",
       "574      5 2010-02-19     303447.57         False        41.14       2.514   \n",
       "717      6 2010-02-19    1567138.07         False        43.58       2.514   \n",
       "860      7 2010-02-19     506760.54         False        27.28       2.550   \n",
       "1003     8 2010-02-19     963960.37         False        39.10       2.514   \n",
       "1146     9 2010-02-19     511327.90         False        43.06       2.514   \n",
       "1289    10 2010-02-19    2113432.58         False        58.22       2.915   \n",
       "1432    11 2010-02-19    1503298.70         False        48.30       2.514   \n",
       "1575    12 2010-02-19    1095421.65         False        54.83       2.915   \n",
       "1718    13 2010-02-19    1970274.64         False        35.70       2.654   \n",
       "1861    14 2010-02-19    2204556.70         False        31.27       2.745   \n",
       "2004    15 2010-02-19     660838.75         False        27.54       2.909   \n",
       "2147    16 2010-02-19     469868.70         False        21.13       2.550   \n",
       "2290    17 2010-02-19     800714.00         False        25.06       2.654   \n",
       "2433    18 2010-02-19    1150663.42         False        30.91       2.747   \n",
       "2576    19 2010-02-19    1515976.11         False        28.57       2.909   \n",
       "2719    20 2010-02-19    2161549.76         False        25.43       2.745   \n",
       "2862    21 2010-02-19     867283.25         False        39.75       2.514   \n",
       "3005    22 2010-02-19     988467.61         False        31.96       2.747   \n",
       "3148    23 2010-02-19    1319588.04         False        26.70       2.747   \n",
       "3291    24 2010-02-19    1385362.49         False        31.05       2.909   \n",
       "3434    25 2010-02-19     676260.67         False        24.16       2.745   \n",
       "3577    26 2010-02-19     999348.55         False        22.62       2.747   \n",
       "3720    27 2010-02-19    1945070.33         False        32.44       2.909   \n",
       "3863    28 2010-02-19    1491300.42         False        54.83       2.915   \n",
       "4006    29 2010-02-19     542399.07         False        31.96       2.747   \n",
       "4149    30 2010-02-19     463513.26         False        39.75       2.514   \n",
       "4292    31 2010-02-19    1473386.75         False        39.75       2.514   \n",
       "4435    32 2010-02-19    1082559.06         False        29.16       2.550   \n",
       "4578    33 2010-02-19     296850.83         False        62.16       2.915   \n",
       "4721    34 2010-02-19     983963.07         False        38.36       2.540   \n",
       "4864    35 2010-02-19    1270658.64         False        32.44       2.745   \n",
       "5007    36 2010-02-19     470281.03         False        45.66       2.472   \n",
       "5150    37 2010-02-19     510382.50         False        45.66       2.514   \n",
       "5293    38 2010-02-19     327237.92         False        54.83       2.915   \n",
       "5436    39 2010-02-19    1230591.97         False        43.96       2.514   \n",
       "5579    40 2010-02-19     916289.20         False        27.84       2.747   \n",
       "5722    41 2010-02-19    1052034.74         False        24.13       2.550   \n",
       "5865    42 2010-02-19     508794.87         False        58.22       2.915   \n",
       "6008    43 2010-02-19     658997.55         False        48.77       2.514   \n",
       "6151    44 2010-02-19     267956.30         False        35.70       2.654   \n",
       "6294    45 2010-02-19     841264.04         False        31.27       2.745   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "2     211.289143         8.106  \n",
       "145   210.945160         8.324  \n",
       "288   214.619887         7.368  \n",
       "431   126.526286         8.623  \n",
       "574   211.847128         6.566  \n",
       "717   212.816155         7.259  \n",
       "860   189.534100         9.014  \n",
       "1003  214.666488         6.299  \n",
       "1146  214.850618         6.415  \n",
       "1289  126.526286         9.765  \n",
       "1432  214.619887         7.368  \n",
       "1575  126.526286        13.975  \n",
       "1718  126.526286         8.316  \n",
       "1861  182.034782         8.992  \n",
       "2004  131.637000         8.350  \n",
       "2147  189.534100         7.039  \n",
       "2290  126.526286         6.548  \n",
       "2433  131.637000         9.202  \n",
       "2576  131.637000         8.350  \n",
       "2719  204.432100         8.187  \n",
       "2862  210.945160         8.324  \n",
       "3005  135.465778         8.283  \n",
       "3148  131.637000         5.892  \n",
       "3291  131.637000         8.326  \n",
       "3434  204.432100         8.187  \n",
       "3577  131.637000         8.488  \n",
       "3720  135.465778         8.237  \n",
       "3863  126.526286        13.975  \n",
       "4006  131.637000        10.064  \n",
       "4149  210.945160         8.324  \n",
       "4292  210.945160         8.324  \n",
       "4435  189.534100         9.014  \n",
       "4578  126.526286        10.115  \n",
       "4721  126.526286         9.521  \n",
       "4864  135.465778         9.262  \n",
       "5007  210.045102         8.554  \n",
       "5150  210.045102         8.554  \n",
       "5293  126.526286        13.975  \n",
       "5436  210.045102         8.554  \n",
       "5579  131.637000         5.892  \n",
       "5722  189.534100         7.541  \n",
       "5865  126.526286         9.765  \n",
       "6008  203.247980         9.521  \n",
       "6151  126.526286         8.119  \n",
       "6294  182.034782         8.992  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Date >= '2010-02-12' and Date <= '2010-02-20'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6816d8",
   "metadata": {},
   "source": [
    "- .iloc acessa por posição (linha,coluna)\n",
    "- .loc permite acessar as variáveis por index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7c65adca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-05-02 00:00:00')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "05905c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0     1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1     1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2     1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "\n",
       "          CPI  Unemployment  \n",
       "0  211.096358         8.106  \n",
       "1  211.242170         8.106  \n",
       "2  211.289143         8.106  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[[0,1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e3c3571c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                             1\n",
       "Date            2010-05-02 00:00:00\n",
       "Weekly_Sales              1643690.9\n",
       "Holiday_Flag                  False\n",
       "Temperature                   42.31\n",
       "Fuel_Price                    2.572\n",
       "CPI                      211.096358\n",
       "Unemployment                  8.106\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4d0c4f",
   "metadata": {},
   "source": [
    "- .loc também permite criar condições para retornar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "12065538",
   "metadata": {},
   "outputs": [
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       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
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       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
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       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
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       "\n",
       "[6435 rows x 8 columns]"
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     },
     "execution_count": 63,
     "metadata": {},
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    "walmart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b1423f3b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>45</td>\n",
       "      <td>2012-07-09</td>\n",
       "      <td>766512.66</td>\n",
       "      <td>True</td>\n",
       "      <td>75.70</td>\n",
       "      <td>3.911</td>\n",
       "      <td>191.577676</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "31       1 2010-10-09    1507460.69          True        78.69       2.565   \n",
       "42       1 2010-11-26    1955624.11          True        64.52       2.735   \n",
       "47       1 2010-12-31    1367320.01          True        48.43       2.943   \n",
       "53       1 2011-11-02    1649614.93          True        36.39       3.022   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6375    45 2011-09-09     746129.56          True        71.48       3.738   \n",
       "6386    45 2011-11-25    1170672.94          True        48.71       3.492   \n",
       "6391    45 2011-12-30     869403.63          True        37.79       3.389   \n",
       "6397    45 2012-10-02     803657.12          True        37.00       3.640   \n",
       "6427    45 2012-07-09     766512.66          True        75.70       3.911   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "1     211.242170         8.106  \n",
       "31    211.495190         7.787  \n",
       "42    211.748433         7.838  \n",
       "47    211.404932         7.838  \n",
       "53    212.936705         7.742  \n",
       "...          ...           ...  \n",
       "6375  186.673738         8.625  \n",
       "6386  188.350400         8.523  \n",
       "6391  189.062016         8.523  \n",
       "6397  189.707605         8.424  \n",
       "6427  191.577676         8.684  \n",
       "\n",
       "[450 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[walmart['Holiday_Flag'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "97a02044",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.638</td>\n",
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      ],
      "text/plain": [
       "   Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "78     1 2011-05-08    1624383.75         False        91.65       3.684   \n",
       "79     1 2011-12-08    1525147.09         False        90.76       3.638   \n",
       "\n",
       "           CPI  Unemployment  \n",
       "78  215.544618         7.962  \n",
       "79  215.605788         7.962  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[(walmart['Temperature'] > 90) & (walmart['Store'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c149e192",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>7</th>\n",
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       "      <td>2010-03-26</td>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "  Store       Date\n",
       "0     1 2010-05-02\n",
       "1     1 2010-12-02\n",
       "2     1 2010-02-19\n",
       "3     1 2010-02-26\n",
       "4     1 2010-05-03\n",
       "5     1 2010-12-03\n",
       "6     1 2010-03-19\n",
       "7     1 2010-03-26\n",
       "8     1 2010-02-04\n",
       "9     1 2010-09-04"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[walmart.index[0:10],[\"Store\", \"Date\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d295ef",
   "metadata": {},
   "source": [
    "- Vamos selecionar as temperaturas máxima e mínima da loja 1.\n",
    "- Usando pipe para auxiliar na filtragem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "af7a6528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91.65, 35.4]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[walmart['Store'] == 1].pipe(lambda x: [x['Temperature'].max(), x['Temperature'].min()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6791a5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91.65, 35.4]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Store==1\").pipe(lambda x: [x['Temperature'].max(), x['Temperature'].min()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ee1255",
   "metadata": {},
   "source": [
    "- Usando eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2b106129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91.65, 35.4]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.loc[walmart['Store'] == 1].eval(\"Temperature.max(), Temperature.min()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a5481fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91.65, 35.4]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walmart.query(\"Store==1\").eval(\"Temperature.max(), Temperature.min()\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71e0e83",
   "metadata": {},
   "source": [
    "- DataFrames permitem fazer copias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "47f244eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2012-05-10</td>\n",
       "      <td>733455.07</td>\n",
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       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
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       "    <tr>\n",
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       "      <td>4.000</td>\n",
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       "      <td>False</td>\n",
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       "      <td>58.85</td>\n",
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       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
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       "<p>6435 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6431    45 2012-05-10     733455.07         False        64.89       3.985   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6433    45 2012-10-19     718125.53         False        56.47       3.969   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "0     211.096358         8.106  \n",
       "1     211.242170         8.106  \n",
       "2     211.289143         8.106  \n",
       "3     211.319643         8.106  \n",
       "4     211.350143         8.106  \n",
       "...          ...           ...  \n",
       "6430  192.013558         8.684  \n",
       "6431  192.170412         8.667  \n",
       "6432  192.327265         8.667  \n",
       "6433  192.330854         8.667  \n",
       "6434  192.308899         8.667  \n",
       "\n",
       "[6435 rows x 8 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = walmart.copy()\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdfd338",
   "metadata": {},
   "source": [
    "- É possível filtrar colunas usando o método filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "80d539d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>734464.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>718125.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>760281.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Weekly_Sales\n",
       "0    2010-05-02    1643690.90\n",
       "1    2010-12-02    1641957.44\n",
       "2    2010-02-19    1611968.17\n",
       "3    2010-02-26    1409727.59\n",
       "4    2010-05-03    1554806.68\n",
       "...         ...           ...\n",
       "6430 2012-09-28     713173.95\n",
       "6431 2012-05-10     733455.07\n",
       "6432 2012-12-10     734464.36\n",
       "6433 2012-10-19     718125.53\n",
       "6434 2012-10-26     760281.43\n",
       "\n",
       "[6435 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.filter(items=['Date','Weekly_Sales'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440907ca",
   "metadata": {},
   "source": [
    "- Selecionar colunas e index com expressão regular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c86cbaf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-05-02</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-02</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-05-03</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6431</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Temperature  Fuel_Price\n",
       "0        1 2010-05-02        42.31       2.572\n",
       "1        1 2010-12-02        38.51       2.548\n",
       "2        1 2010-02-19        39.93       2.514\n",
       "3        1 2010-02-26        46.63       2.561\n",
       "4        1 2010-05-03        46.50       2.625\n",
       "...    ...        ...          ...         ...\n",
       "6430    45 2012-09-28        64.88       3.997\n",
       "6431    45 2012-05-10        64.89       3.985\n",
       "6432    45 2012-12-10        54.47       4.000\n",
       "6433    45 2012-10-19        56.47       3.969\n",
       "6434    45 2012-10-26        58.85       3.882\n",
       "\n",
       "[6435 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando as colunas que terminam com e\n",
    "df_aux.filter(regex='e$', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "727ad4e1",
   "metadata": {},
   "source": [
    "- Filtrar por caracteres colunas e index utilizando like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d0d19fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>66.32</td>\n",
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       "      <td>1319325.59</td>\n",
       "      <td>False</td>\n",
       "      <td>54.26</td>\n",
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       "      <td>587685.38</td>\n",
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       "      <td>211.587991</td>\n",
       "      <td>9.653</td>\n",
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       "    <tr>\n",
       "      <th>6110</th>\n",
       "      <td>43</td>\n",
       "      <td>2012-03-02</td>\n",
       "      <td>629176.71</td>\n",
       "      <td>False</td>\n",
       "      <td>51.92</td>\n",
       "      <td>3.360</td>\n",
       "      <td>211.676200</td>\n",
       "      <td>9.653</td>\n",
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       "    <tr>\n",
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       "      <td>44</td>\n",
       "      <td>2011-08-04</td>\n",
       "      <td>292498.61</td>\n",
       "      <td>False</td>\n",
       "      <td>42.75</td>\n",
       "      <td>3.547</td>\n",
       "      <td>128.823806</td>\n",
       "      <td>6.906</td>\n",
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       "    <tr>\n",
       "      <th>6310</th>\n",
       "      <td>45</td>\n",
       "      <td>2010-11-06</td>\n",
       "      <td>794698.77</td>\n",
       "      <td>False</td>\n",
       "      <td>69.71</td>\n",
       "      <td>2.809</td>\n",
       "      <td>182.431557</td>\n",
       "      <td>8.899</td>\n",
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       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>770487.37</td>\n",
       "      <td>False</td>\n",
       "      <td>61.24</td>\n",
       "      <td>3.889</td>\n",
       "      <td>190.976417</td>\n",
       "      <td>8.567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "10       1 2010-04-16    1466058.28         False        66.32       2.808   \n",
       "100      1 2012-06-01    1550369.92         False        49.01       3.157   \n",
       "101      1 2012-01-13    1459601.17         False        48.53       3.261   \n",
       "102      1 2012-01-20    1394393.84         False        54.11       3.268   \n",
       "103      1 2012-01-27    1319325.59         False        54.26       3.290   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6109    43 2012-01-27     587685.38         False        52.10       3.290   \n",
       "6110    43 2012-03-02     629176.71         False        51.92       3.360   \n",
       "6210    44 2011-08-04     292498.61         False        42.75       3.547   \n",
       "6310    45 2010-11-06     794698.77         False        69.71       2.809   \n",
       "6410    45 2012-11-05     770487.37         False        61.24       3.889   \n",
       "\n",
       "             CPI  Unemployment  \n",
       "10    210.488700         7.808  \n",
       "100   219.714258         7.348  \n",
       "101   219.892526         7.348  \n",
       "102   219.985689         7.348  \n",
       "103   220.078852         7.348  \n",
       "...          ...           ...  \n",
       "6109  211.587991         9.653  \n",
       "6110  211.676200         9.653  \n",
       "6210  128.823806         6.906  \n",
       "6310  182.431557         8.899  \n",
       "6410  190.976417         8.567  \n",
       "\n",
       "[234 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtrando os indexes que possuem '10'\n",
    "df_aux.filter(like='10', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3ae00944",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>6431</th>\n",
       "      <td>45</td>\n",
       "      <td>64.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>54.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>45</td>\n",
       "      <td>56.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>45</td>\n",
       "      <td>58.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store  Temperature\n",
       "0        1        42.31\n",
       "1        1        38.51\n",
       "2        1        39.93\n",
       "3        1        46.63\n",
       "4        1        46.50\n",
       "...    ...          ...\n",
       "6430    45        64.88\n",
       "6431    45        64.89\n",
       "6432    45        54.47\n",
       "6433    45        56.47\n",
       "6434    45        58.85\n",
       "\n",
       "[6435 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtrando as colunas que possuem 're'\n",
    "df_aux.filter(like='re', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e605c1c",
   "metadata": {},
   "source": [
    "- Podemos utilizar del para excluir colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "21739696",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>6430</th>\n",
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       "      <td>713173.95</td>\n",
       "      <td>False</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>8.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6431</th>\n",
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       "      <td>2012-05-10</td>\n",
       "      <td>733455.07</td>\n",
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       "      <td>3.985</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
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       "      <td>734464.36</td>\n",
       "      <td>False</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
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       "      <td>718125.53</td>\n",
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       "    <tr>\n",
       "      <th>6434</th>\n",
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       "      <td>2012-10-26</td>\n",
       "      <td>760281.43</td>\n",
       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6431    45 2012-05-10     733455.07         False        64.89       3.985   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6433    45 2012-10-19     718125.53         False        56.47       3.969   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "      Unemployment  \n",
       "0            8.106  \n",
       "1            8.106  \n",
       "2            8.106  \n",
       "3            8.106  \n",
       "4            8.106  \n",
       "...            ...  \n",
       "6430         8.684  \n",
       "6431         8.667  \n",
       "6432         8.667  \n",
       "6433         8.667  \n",
       "6434         8.667  \n",
       "\n",
       "[6435 rows x 7 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df_aux['CPI']\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a88964b",
   "metadata": {},
   "source": [
    "- Já para excluir linhas devemos utilizar o drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2499e4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "5        1 2010-12-03    1439541.59         False        57.79       2.667   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6431    45 2012-05-10     733455.07         False        64.89       3.985   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6433    45 2012-10-19     718125.53         False        56.47       3.969   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "      Unemployment  \n",
       "1            8.106  \n",
       "2            8.106  \n",
       "3            8.106  \n",
       "4            8.106  \n",
       "5            8.106  \n",
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       "6431         8.667  \n",
       "6432         8.667  \n",
       "6433         8.667  \n",
       "6434         8.667  \n",
       "\n",
       "[6434 rows x 7 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c86eed4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6430</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-09-28</td>\n",
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       "      <td>False</td>\n",
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       "      <td>3.997</td>\n",
       "      <td>8.684</td>\n",
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       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>734464.36</td>\n",
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       "      <td>760281.43</td>\n",
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       "      <td>8.667</td>\n",
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       "<p>6433 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6428    45 2012-09-14     702238.27         False        67.87       3.948   \n",
       "6429    45 2012-09-21     723086.20         False        65.32       4.038   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "      Unemployment  \n",
       "0            8.106  \n",
       "1            8.106  \n",
       "2            8.106  \n",
       "3            8.106  \n",
       "4            8.106  \n",
       "...            ...  \n",
       "6428         8.684  \n",
       "6429         8.684  \n",
       "6430         8.684  \n",
       "6432         8.667  \n",
       "6434         8.667  \n",
       "\n",
       "[6433 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.drop([6431,6433])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f324bc5c",
   "metadata": {},
   "source": [
    "- Perceba que drop retorna um novo objeto removendo o que foi passado por paramentro. Mas o objeto original não é modificado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f97d5f23",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1554806.68</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
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       "      <td>3.997</td>\n",
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       "      <th>6431</th>\n",
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       "      <th>6432</th>\n",
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       "    <tr>\n",
       "      <th>6433</th>\n",
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       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>8.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6435 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6431    45 2012-05-10     733455.07         False        64.89       3.985   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6433    45 2012-10-19     718125.53         False        56.47       3.969   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "      Unemployment  \n",
       "0            8.106  \n",
       "1            8.106  \n",
       "2            8.106  \n",
       "3            8.106  \n",
       "4            8.106  \n",
       "...            ...  \n",
       "6430         8.684  \n",
       "6431         8.667  \n",
       "6432         8.667  \n",
       "6433         8.667  \n",
       "6434         8.667  \n",
       "\n",
       "[6435 rows x 7 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1401292e",
   "metadata": {},
   "source": [
    "- Para realizar a operação no mesmo objeto é necessário passar True no parametro inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4f14c1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0        1 2010-05-02    1643690.90         False        42.31       2.572   \n",
       "1        1 2010-12-02    1641957.44          True        38.51       2.548   \n",
       "2        1 2010-02-19    1611968.17         False        39.93       2.514   \n",
       "3        1 2010-02-26    1409727.59         False        46.63       2.561   \n",
       "4        1 2010-05-03    1554806.68         False        46.50       2.625   \n",
       "...    ...        ...           ...           ...          ...         ...   \n",
       "6428    45 2012-09-14     702238.27         False        67.87       3.948   \n",
       "6429    45 2012-09-21     723086.20         False        65.32       4.038   \n",
       "6430    45 2012-09-28     713173.95         False        64.88       3.997   \n",
       "6432    45 2012-12-10     734464.36         False        54.47       4.000   \n",
       "6434    45 2012-10-26     760281.43         False        58.85       3.882   \n",
       "\n",
       "      Unemployment  \n",
       "0            8.106  \n",
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       "2            8.106  \n",
       "3            8.106  \n",
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       "6429         8.684  \n",
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       "6434         8.667  \n",
       "\n",
       "[6433 rows x 7 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.drop([6431,6433], inplace=True)\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c7c7a23",
   "metadata": {},
   "source": [
    "- Drop também permite a remoção de colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "47d3a20c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6433 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store       Date  Weekly_Sales  Holiday_Flag  Temperature\n",
       "0        1 2010-05-02    1643690.90         False        42.31\n",
       "1        1 2010-12-02    1641957.44          True        38.51\n",
       "2        1 2010-02-19    1611968.17         False        39.93\n",
       "3        1 2010-02-26    1409727.59         False        46.63\n",
       "4        1 2010-05-03    1554806.68         False        46.50\n",
       "...    ...        ...           ...           ...          ...\n",
       "6428    45 2012-09-14     702238.27         False        67.87\n",
       "6429    45 2012-09-21     723086.20         False        65.32\n",
       "6430    45 2012-09-28     713173.95         False        64.88\n",
       "6432    45 2012-12-10     734464.36         False        54.47\n",
       "6434    45 2012-10-26     760281.43         False        58.85\n",
       "\n",
       "[6433 rows x 5 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux.drop(['Fuel_Price','Unemployment'], inplace=True, axis='columns')\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7dcebf4",
   "metadata": {},
   "source": [
    "- É possível também adicionar um nome para o index e para as coluna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9d2856be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aux.index.name = 'indice'\n",
    "df_aux.columns.name = 'dados'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9a1475c4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>65.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>713173.95</td>\n",
       "      <td>False</td>\n",
       "      <td>64.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>734464.36</td>\n",
       "      <td>False</td>\n",
       "      <td>54.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6434</th>\n",
       "      <td>45</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>760281.43</td>\n",
       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6433 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "dados  Store       Date  Weekly_Sales  Holiday_Flag  Temperature\n",
       "indice                                                          \n",
       "0          1 2010-05-02    1643690.90         False        42.31\n",
       "1          1 2010-12-02    1641957.44          True        38.51\n",
       "2          1 2010-02-19    1611968.17         False        39.93\n",
       "3          1 2010-02-26    1409727.59         False        46.63\n",
       "4          1 2010-05-03    1554806.68         False        46.50\n",
       "...      ...        ...           ...           ...          ...\n",
       "6428      45 2012-09-14     702238.27         False        67.87\n",
       "6429      45 2012-09-21     723086.20         False        65.32\n",
       "6430      45 2012-09-28     713173.95         False        64.88\n",
       "6432      45 2012-12-10     734464.36         False        54.47\n",
       "6434      45 2012-10-26     760281.43         False        58.85\n",
       "\n",
       "[6433 rows x 5 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c428625",
   "metadata": {},
   "source": [
    "- Analisando Store 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ff658059",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>1409727.59</td>\n",
       "      <td>False</td>\n",
       "      <td>46.63</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>1437059.26</td>\n",
       "      <td>False</td>\n",
       "      <td>76.08</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-05-10</td>\n",
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       "      <td>2012-12-10</td>\n",
       "      <td>1573072.81</td>\n",
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       "      <td>62.99</td>\n",
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       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>1508068.77</td>\n",
       "      <td>False</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>1493659.74</td>\n",
       "      <td>False</td>\n",
       "      <td>69.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "dados  Store       Date  Weekly_Sales  Holiday_Flag  Temperature\n",
       "indice                                                          \n",
       "0          1 2010-05-02    1643690.90         False        42.31\n",
       "1          1 2010-12-02    1641957.44          True        38.51\n",
       "2          1 2010-02-19    1611968.17         False        39.93\n",
       "3          1 2010-02-26    1409727.59         False        46.63\n",
       "4          1 2010-05-03    1554806.68         False        46.50\n",
       "...      ...        ...           ...           ...          ...\n",
       "138        1 2012-09-28    1437059.26         False        76.08\n",
       "139        1 2012-05-10    1670785.97         False        68.55\n",
       "140        1 2012-12-10    1573072.81         False        62.99\n",
       "141        1 2012-10-19    1508068.77         False        67.97\n",
       "142        1 2012-10-26    1493659.74         False        69.16\n",
       "\n",
       "[143 rows x 5 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = df_aux.query(\"Store==1\")\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56dae7c5",
   "metadata": {},
   "source": [
    "- O método assign atribui novas colunas ao DataFrame, podendo até sobrescrevê-la caso a coluna já exista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "905b69e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Saturday: Weekend (Not working day)</td>\n",
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       "      <td>Sunday: Weekend (Not working day)</td>\n",
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       "      <th>2</th>\n",
       "      <td>Monday: Doctor appointment at 2:45pm.</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>Tuesday: Dentist appointment at 11:30 am.</td>\n",
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       "      <th>4</th>\n",
       "      <td>Wednesday: basketball game At 7:00pm</td>\n",
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       "      <td>Thursday: Back home by 11:15 pm.</td>\n",
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       "      <td>Friday: Take the train at 08:10 am.</td>\n",
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      ],
      "text/plain": [
       "                                        text\n",
       "0        Saturday: Weekend (Not working day)\n",
       "1          Sunday: Weekend (Not working day)\n",
       "2      Monday: Doctor appointment at 2:45pm.\n",
       "3  Tuesday: Dentist appointment at 11:30 am.\n",
       "4       Wednesday: basketball game At 7:00pm\n",
       "5           Thursday: Back home by 11:15 pm.\n",
       "6        Friday: Take the train at 08:10 am."
      ]
     },
     "execution_count": 85,
     "metadata": {},
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    }
   ],
   "source": [
    "time_sentences = [\"Saturday: Weekend (Not working day)\",\n",
    "                  \"Sunday: Weekend (Not working day)\",\n",
    "                  \"Monday: Doctor appointment at 2:45pm.\",\n",
    "                  \"Tuesday: Dentist appointment at 11:30 am.\",\n",
    "                  \"Wednesday: basketball game At 7:00pm\",\n",
    "                  \"Thursday: Back home by 11:15 pm.\",\n",
    "                  \"Friday: Take the train at 08:10 am.\"]\n",
    "\n",
    "df_sentences = pd.DataFrame(time_sentences, columns=['text'])\n",
    "df_sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f650df0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>tuesday: dentist appointment at 11:30 am.</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>wednesday: basketball game at 7:00pm</td>\n",
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       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>thursday: back home by 11:15 pm.</td>\n",
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       "      <td>False</td>\n",
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       "      <th>6</th>\n",
       "      <td>friday: take the train at 08:10 am.</td>\n",
       "      <td>35</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "                                        text  text_len  word_count  weekend\n",
       "0        saturday: weekend (not working day)        35           5     True\n",
       "1          sunday: weekend (not working day)        33           5     True\n",
       "2      monday: doctor appointment at 2:45pm.        37           5    False\n",
       "3  tuesday: dentist appointment at 11:30 am.        41           6    False\n",
       "4       wednesday: basketball game at 7:00pm        36           5    False\n",
       "5           thursday: back home by 11:15 pm.        32           6    False\n",
       "6        friday: take the train at 08:10 am.        35           7    False"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sentences = df_sentences.assign(text=df_sentences.text.str.lower(),\n",
    "                                    text_len=df_sentences.text.str.len(),\n",
    "                                    word_count=df_sentences.text.str.count(\" \") + 1,\n",
    "                                    weekend=df_sentences.text.str.contains(\"saturday|sunday\", case=False)\n",
    "                                )\n",
    "df_sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd0b040",
   "metadata": {},
   "source": [
    "- Renomeando as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bf5aaaee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>loja</th>\n",
       "      <th>data</th>\n",
       "      <th>vendas_semanais</th>\n",
       "      <th>feriado</th>\n",
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       "      <td>46.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>138</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>1437059.26</td>\n",
       "      <td>False</td>\n",
       "      <td>76.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-05-10</td>\n",
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       "      <th>140</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-12-10</td>\n",
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       "      <td>False</td>\n",
       "      <td>62.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>1508068.77</td>\n",
       "      <td>False</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>1493659.74</td>\n",
       "      <td>False</td>\n",
       "      <td>69.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "dados  loja       data  vendas_semanais  feriado  temperatura\n",
       "indice                                                       \n",
       "0         1 2010-05-02       1643690.90    False        42.31\n",
       "1         1 2010-12-02       1641957.44     True        38.51\n",
       "2         1 2010-02-19       1611968.17    False        39.93\n",
       "3         1 2010-02-26       1409727.59    False        46.63\n",
       "4         1 2010-05-03       1554806.68    False        46.50\n",
       "...     ...        ...              ...      ...          ...\n",
       "138       1 2012-09-28       1437059.26    False        76.08\n",
       "139       1 2012-05-10       1670785.97    False        68.55\n",
       "140       1 2012-12-10       1573072.81    False        62.99\n",
       "141       1 2012-10-19       1508068.77    False        67.97\n",
       "142       1 2012-10-26       1493659.74    False        69.16\n",
       "\n",
       "[143 rows x 5 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aux = df_aux.rename({\"Store\": \"loja\", \n",
    "                        \"Date\": \"data\", \n",
    "                        \"Weekly_Sales\": \"vendas_semanais\",\n",
    "                        \"Holiday_Flag\": \"feriado\",\n",
    "                        \"Temperature\": \"temperatura\"\n",
    "                       }, axis=1)\n",
    "df_aux"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1700589",
   "metadata": {},
   "source": [
    "- Alterando formatação dos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bc997d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7b638\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >dados</th>\n",
       "      <th id=\"T_7b638_level0_col0\" class=\"col_heading level0 col0\" >loja</th>\n",
       "      <th id=\"T_7b638_level0_col1\" class=\"col_heading level0 col1\" >data</th>\n",
       "      <th id=\"T_7b638_level0_col2\" class=\"col_heading level0 col2\" >vendas_semanais</th>\n",
       "      <th id=\"T_7b638_level0_col3\" class=\"col_heading level0 col3\" >feriado</th>\n",
       "      <th id=\"T_7b638_level0_col4\" class=\"col_heading level0 col4\" >temperatura</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >indice</th>\n",
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       "      <th id=\"T_7b638_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7b638_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row0_col1\" class=\"data row0 col1\" >02/05/10</td>\n",
       "      <td id=\"T_7b638_row0_col2\" class=\"data row0 col2\" >$ 1643690.90</td>\n",
       "      <td id=\"T_7b638_row0_col3\" class=\"data row0 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row0_col4\" class=\"data row0 col4\" >42.31º</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_7b638_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row1_col1\" class=\"data row1 col1\" >02/12/10</td>\n",
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       "      <td id=\"T_7b638_row1_col3\" class=\"data row1 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row1_col4\" class=\"data row1 col4\" >38.51º</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_7b638_row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row2_col1\" class=\"data row2 col1\" >19/02/10</td>\n",
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       "      <td id=\"T_7b638_row2_col3\" class=\"data row2 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row2_col4\" class=\"data row2 col4\" >39.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7b638_row3_col0\" class=\"data row3 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row3_col1\" class=\"data row3 col1\" >26/02/10</td>\n",
       "      <td id=\"T_7b638_row3_col2\" class=\"data row3 col2\" >$ 1409727.59</td>\n",
       "      <td id=\"T_7b638_row3_col3\" class=\"data row3 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row3_col4\" class=\"data row3 col4\" >46.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7b638_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row4_col1\" class=\"data row4 col1\" >03/05/10</td>\n",
       "      <td id=\"T_7b638_row4_col2\" class=\"data row4 col2\" >$ 1554806.68</td>\n",
       "      <td id=\"T_7b638_row4_col3\" class=\"data row4 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row4_col4\" class=\"data row4 col4\" >46.50º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7b638_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row5_col1\" class=\"data row5 col1\" >03/12/10</td>\n",
       "      <td id=\"T_7b638_row5_col2\" class=\"data row5 col2\" >$ 1439541.59</td>\n",
       "      <td id=\"T_7b638_row5_col3\" class=\"data row5 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row5_col4\" class=\"data row5 col4\" >57.79º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7b638_row6_col0\" class=\"data row6 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row6_col1\" class=\"data row6 col1\" >19/03/10</td>\n",
       "      <td id=\"T_7b638_row6_col2\" class=\"data row6 col2\" >$ 1472515.79</td>\n",
       "      <td id=\"T_7b638_row6_col3\" class=\"data row6 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row6_col4\" class=\"data row6 col4\" >54.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7b638_row7_col0\" class=\"data row7 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row7_col1\" class=\"data row7 col1\" >26/03/10</td>\n",
       "      <td id=\"T_7b638_row7_col2\" class=\"data row7 col2\" >$ 1404429.92</td>\n",
       "      <td id=\"T_7b638_row7_col3\" class=\"data row7 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row7_col4\" class=\"data row7 col4\" >51.45º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7b638_row8_col0\" class=\"data row8 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row8_col1\" class=\"data row8 col1\" >04/02/10</td>\n",
       "      <td id=\"T_7b638_row8_col2\" class=\"data row8 col2\" >$ 1594968.28</td>\n",
       "      <td id=\"T_7b638_row8_col3\" class=\"data row8 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row8_col4\" class=\"data row8 col4\" >62.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7b638_row9_col0\" class=\"data row9 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row9_col1\" class=\"data row9 col1\" >04/09/10</td>\n",
       "      <td id=\"T_7b638_row9_col2\" class=\"data row9 col2\" >$ 1545418.53</td>\n",
       "      <td id=\"T_7b638_row9_col3\" class=\"data row9 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row9_col4\" class=\"data row9 col4\" >65.86º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7b638_row10_col0\" class=\"data row10 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row10_col1\" class=\"data row10 col1\" >16/04/10</td>\n",
       "      <td id=\"T_7b638_row10_col2\" class=\"data row10 col2\" >$ 1466058.28</td>\n",
       "      <td id=\"T_7b638_row10_col3\" class=\"data row10 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row10_col4\" class=\"data row10 col4\" >66.32º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7b638_row11_col0\" class=\"data row11 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row11_col1\" class=\"data row11 col1\" >23/04/10</td>\n",
       "      <td id=\"T_7b638_row11_col2\" class=\"data row11 col2\" >$ 1391256.12</td>\n",
       "      <td id=\"T_7b638_row11_col3\" class=\"data row11 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row11_col4\" class=\"data row11 col4\" >64.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7b638_row12_col0\" class=\"data row12 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row12_col1\" class=\"data row12 col1\" >30/04/10</td>\n",
       "      <td id=\"T_7b638_row12_col2\" class=\"data row12 col2\" >$ 1425100.71</td>\n",
       "      <td id=\"T_7b638_row12_col3\" class=\"data row12 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row12_col4\" class=\"data row12 col4\" >67.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7b638_row13_col0\" class=\"data row13 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row13_col1\" class=\"data row13 col1\" >05/07/10</td>\n",
       "      <td id=\"T_7b638_row13_col2\" class=\"data row13 col2\" >$ 1603955.12</td>\n",
       "      <td id=\"T_7b638_row13_col3\" class=\"data row13 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row13_col4\" class=\"data row13 col4\" >72.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7b638_row14_col0\" class=\"data row14 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row14_col1\" class=\"data row14 col1\" >14/05/10</td>\n",
       "      <td id=\"T_7b638_row14_col2\" class=\"data row14 col2\" >$ 1494251.50</td>\n",
       "      <td id=\"T_7b638_row14_col3\" class=\"data row14 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row14_col4\" class=\"data row14 col4\" >74.78º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7b638_row15_col0\" class=\"data row15 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row15_col1\" class=\"data row15 col1\" >21/05/10</td>\n",
       "      <td id=\"T_7b638_row15_col2\" class=\"data row15 col2\" >$ 1399662.07</td>\n",
       "      <td id=\"T_7b638_row15_col3\" class=\"data row15 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row15_col4\" class=\"data row15 col4\" >76.44º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7b638_row16_col0\" class=\"data row16 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row16_col1\" class=\"data row16 col1\" >28/05/10</td>\n",
       "      <td id=\"T_7b638_row16_col2\" class=\"data row16 col2\" >$ 1432069.95</td>\n",
       "      <td id=\"T_7b638_row16_col3\" class=\"data row16 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row16_col4\" class=\"data row16 col4\" >80.44º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7b638_row17_col0\" class=\"data row17 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row17_col1\" class=\"data row17 col1\" >06/04/10</td>\n",
       "      <td id=\"T_7b638_row17_col2\" class=\"data row17 col2\" >$ 1615524.71</td>\n",
       "      <td id=\"T_7b638_row17_col3\" class=\"data row17 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row17_col4\" class=\"data row17 col4\" >80.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_7b638_row18_col0\" class=\"data row18 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row18_col1\" class=\"data row18 col1\" >06/11/10</td>\n",
       "      <td id=\"T_7b638_row18_col2\" class=\"data row18 col2\" >$ 1542561.09</td>\n",
       "      <td id=\"T_7b638_row18_col3\" class=\"data row18 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row18_col4\" class=\"data row18 col4\" >80.43º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_7b638_row19_col0\" class=\"data row19 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row19_col1\" class=\"data row19 col1\" >18/06/10</td>\n",
       "      <td id=\"T_7b638_row19_col2\" class=\"data row19 col2\" >$ 1503284.06</td>\n",
       "      <td id=\"T_7b638_row19_col3\" class=\"data row19 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row19_col4\" class=\"data row19 col4\" >84.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_7b638_row20_col0\" class=\"data row20 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row20_col1\" class=\"data row20 col1\" >25/06/10</td>\n",
       "      <td id=\"T_7b638_row20_col2\" class=\"data row20 col2\" >$ 1422711.60</td>\n",
       "      <td id=\"T_7b638_row20_col3\" class=\"data row20 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row20_col4\" class=\"data row20 col4\" >84.34º</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_7b638_row21_col0\" class=\"data row21 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row21_col1\" class=\"data row21 col1\" >07/02/10</td>\n",
       "      <td id=\"T_7b638_row21_col2\" class=\"data row21 col2\" >$ 1492418.14</td>\n",
       "      <td id=\"T_7b638_row21_col3\" class=\"data row21 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row21_col4\" class=\"data row21 col4\" >80.91º</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_7b638_row22_col0\" class=\"data row22 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row22_col1\" class=\"data row22 col1\" >07/09/10</td>\n",
       "      <td id=\"T_7b638_row22_col2\" class=\"data row22 col2\" >$ 1546074.18</td>\n",
       "      <td id=\"T_7b638_row22_col3\" class=\"data row22 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row22_col4\" class=\"data row22 col4\" >80.48º</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_7b638_row23_col0\" class=\"data row23 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row23_col1\" class=\"data row23 col1\" >16/07/10</td>\n",
       "      <td id=\"T_7b638_row23_col2\" class=\"data row23 col2\" >$ 1448938.92</td>\n",
       "      <td id=\"T_7b638_row23_col3\" class=\"data row23 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row23_col4\" class=\"data row23 col4\" >83.15º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_7b638_row24_col0\" class=\"data row24 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row24_col1\" class=\"data row24 col1\" >23/07/10</td>\n",
       "      <td id=\"T_7b638_row24_col2\" class=\"data row24 col2\" >$ 1385065.20</td>\n",
       "      <td id=\"T_7b638_row24_col3\" class=\"data row24 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row24_col4\" class=\"data row24 col4\" >83.36º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_7b638_row25_col0\" class=\"data row25 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row25_col1\" class=\"data row25 col1\" >30/07/10</td>\n",
       "      <td id=\"T_7b638_row25_col2\" class=\"data row25 col2\" >$ 1371986.60</td>\n",
       "      <td id=\"T_7b638_row25_col3\" class=\"data row25 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row25_col4\" class=\"data row25 col4\" >81.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_7b638_row26_col0\" class=\"data row26 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row26_col1\" class=\"data row26 col1\" >08/06/10</td>\n",
       "      <td id=\"T_7b638_row26_col2\" class=\"data row26 col2\" >$ 1605491.78</td>\n",
       "      <td id=\"T_7b638_row26_col3\" class=\"data row26 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row26_col4\" class=\"data row26 col4\" >87.16º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_7b638_row27_col0\" class=\"data row27 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row27_col1\" class=\"data row27 col1\" >13/08/10</td>\n",
       "      <td id=\"T_7b638_row27_col2\" class=\"data row27 col2\" >$ 1508237.76</td>\n",
       "      <td id=\"T_7b638_row27_col3\" class=\"data row27 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row27_col4\" class=\"data row27 col4\" >87.00º</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_7b638_row28_col0\" class=\"data row28 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row28_col1\" class=\"data row28 col1\" >20/08/10</td>\n",
       "      <td id=\"T_7b638_row28_col2\" class=\"data row28 col2\" >$ 1513080.49</td>\n",
       "      <td id=\"T_7b638_row28_col3\" class=\"data row28 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row28_col4\" class=\"data row28 col4\" >86.65º</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_7b638_row29_col0\" class=\"data row29 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row29_col1\" class=\"data row29 col1\" >27/08/10</td>\n",
       "      <td id=\"T_7b638_row29_col2\" class=\"data row29 col2\" >$ 1449142.92</td>\n",
       "      <td id=\"T_7b638_row29_col3\" class=\"data row29 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row29_col4\" class=\"data row29 col4\" >85.22º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_7b638_row30_col0\" class=\"data row30 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row30_col1\" class=\"data row30 col1\" >09/03/10</td>\n",
       "      <td id=\"T_7b638_row30_col2\" class=\"data row30 col2\" >$ 1540163.53</td>\n",
       "      <td id=\"T_7b638_row30_col3\" class=\"data row30 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row30_col4\" class=\"data row30 col4\" >81.21º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_7b638_row31_col0\" class=\"data row31 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row31_col1\" class=\"data row31 col1\" >09/10/10</td>\n",
       "      <td id=\"T_7b638_row31_col2\" class=\"data row31 col2\" >$ 1507460.69</td>\n",
       "      <td id=\"T_7b638_row31_col3\" class=\"data row31 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row31_col4\" class=\"data row31 col4\" >78.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_7b638_row32_col0\" class=\"data row32 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row32_col1\" class=\"data row32 col1\" >17/09/10</td>\n",
       "      <td id=\"T_7b638_row32_col2\" class=\"data row32 col2\" >$ 1430378.67</td>\n",
       "      <td id=\"T_7b638_row32_col3\" class=\"data row32 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row32_col4\" class=\"data row32 col4\" >82.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_7b638_row33_col0\" class=\"data row33 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row33_col1\" class=\"data row33 col1\" >24/09/10</td>\n",
       "      <td id=\"T_7b638_row33_col2\" class=\"data row33 col2\" >$ 1351791.03</td>\n",
       "      <td id=\"T_7b638_row33_col3\" class=\"data row33 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row33_col4\" class=\"data row33 col4\" >80.94º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_7b638_row34_col0\" class=\"data row34 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row34_col1\" class=\"data row34 col1\" >10/01/10</td>\n",
       "      <td id=\"T_7b638_row34_col2\" class=\"data row34 col2\" >$ 1453329.50</td>\n",
       "      <td id=\"T_7b638_row34_col3\" class=\"data row34 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row34_col4\" class=\"data row34 col4\" >71.89º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_7b638_row35_col0\" class=\"data row35 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row35_col1\" class=\"data row35 col1\" >10/08/10</td>\n",
       "      <td id=\"T_7b638_row35_col2\" class=\"data row35 col2\" >$ 1508239.93</td>\n",
       "      <td id=\"T_7b638_row35_col3\" class=\"data row35 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row35_col4\" class=\"data row35 col4\" >63.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_7b638_row36_col0\" class=\"data row36 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row36_col1\" class=\"data row36 col1\" >15/10/10</td>\n",
       "      <td id=\"T_7b638_row36_col2\" class=\"data row36 col2\" >$ 1459409.10</td>\n",
       "      <td id=\"T_7b638_row36_col3\" class=\"data row36 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row36_col4\" class=\"data row36 col4\" >67.18º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_7b638_row37_col0\" class=\"data row37 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row37_col1\" class=\"data row37 col1\" >22/10/10</td>\n",
       "      <td id=\"T_7b638_row37_col2\" class=\"data row37 col2\" >$ 1345454.00</td>\n",
       "      <td id=\"T_7b638_row37_col3\" class=\"data row37 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row37_col4\" class=\"data row37 col4\" >69.86º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_7b638_row38_col0\" class=\"data row38 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row38_col1\" class=\"data row38 col1\" >29/10/10</td>\n",
       "      <td id=\"T_7b638_row38_col2\" class=\"data row38 col2\" >$ 1384209.22</td>\n",
       "      <td id=\"T_7b638_row38_col3\" class=\"data row38 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row38_col4\" class=\"data row38 col4\" >69.64º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_7b638_row39_col0\" class=\"data row39 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row39_col1\" class=\"data row39 col1\" >11/05/10</td>\n",
       "      <td id=\"T_7b638_row39_col2\" class=\"data row39 col2\" >$ 1551659.28</td>\n",
       "      <td id=\"T_7b638_row39_col3\" class=\"data row39 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row39_col4\" class=\"data row39 col4\" >58.74º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_7b638_row40_col0\" class=\"data row40 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row40_col1\" class=\"data row40 col1\" >11/12/10</td>\n",
       "      <td id=\"T_7b638_row40_col2\" class=\"data row40 col2\" >$ 1494479.49</td>\n",
       "      <td id=\"T_7b638_row40_col3\" class=\"data row40 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row40_col4\" class=\"data row40 col4\" >59.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_7b638_row41_col0\" class=\"data row41 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row41_col1\" class=\"data row41 col1\" >19/11/10</td>\n",
       "      <td id=\"T_7b638_row41_col2\" class=\"data row41 col2\" >$ 1483784.18</td>\n",
       "      <td id=\"T_7b638_row41_col3\" class=\"data row41 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row41_col4\" class=\"data row41 col4\" >51.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_7b638_row42_col0\" class=\"data row42 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row42_col1\" class=\"data row42 col1\" >26/11/10</td>\n",
       "      <td id=\"T_7b638_row42_col2\" class=\"data row42 col2\" >$ 1955624.11</td>\n",
       "      <td id=\"T_7b638_row42_col3\" class=\"data row42 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row42_col4\" class=\"data row42 col4\" >64.52º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_7b638_row43_col0\" class=\"data row43 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row43_col1\" class=\"data row43 col1\" >12/03/10</td>\n",
       "      <td id=\"T_7b638_row43_col2\" class=\"data row43 col2\" >$ 1548033.78</td>\n",
       "      <td id=\"T_7b638_row43_col3\" class=\"data row43 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row43_col4\" class=\"data row43 col4\" >49.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_7b638_row44_col0\" class=\"data row44 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row44_col1\" class=\"data row44 col1\" >12/10/10</td>\n",
       "      <td id=\"T_7b638_row44_col2\" class=\"data row44 col2\" >$ 1682614.26</td>\n",
       "      <td id=\"T_7b638_row44_col3\" class=\"data row44 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row44_col4\" class=\"data row44 col4\" >46.33º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_7b638_row45_col0\" class=\"data row45 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row45_col1\" class=\"data row45 col1\" >17/12/10</td>\n",
       "      <td id=\"T_7b638_row45_col2\" class=\"data row45 col2\" >$ 1891034.93</td>\n",
       "      <td id=\"T_7b638_row45_col3\" class=\"data row45 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row45_col4\" class=\"data row45 col4\" >49.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_7b638_row46_col0\" class=\"data row46 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row46_col1\" class=\"data row46 col1\" >24/12/10</td>\n",
       "      <td id=\"T_7b638_row46_col2\" class=\"data row46 col2\" >$ 2387950.20</td>\n",
       "      <td id=\"T_7b638_row46_col3\" class=\"data row46 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row46_col4\" class=\"data row46 col4\" >52.33º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_7b638_row47_col0\" class=\"data row47 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row47_col1\" class=\"data row47 col1\" >31/12/10</td>\n",
       "      <td id=\"T_7b638_row47_col2\" class=\"data row47 col2\" >$ 1367320.01</td>\n",
       "      <td id=\"T_7b638_row47_col3\" class=\"data row47 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row47_col4\" class=\"data row47 col4\" >48.43º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_7b638_row48_col0\" class=\"data row48 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row48_col1\" class=\"data row48 col1\" >01/07/11</td>\n",
       "      <td id=\"T_7b638_row48_col2\" class=\"data row48 col2\" >$ 1444732.28</td>\n",
       "      <td id=\"T_7b638_row48_col3\" class=\"data row48 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row48_col4\" class=\"data row48 col4\" >48.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_7b638_row49_col0\" class=\"data row49 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row49_col1\" class=\"data row49 col1\" >14/01/11</td>\n",
       "      <td id=\"T_7b638_row49_col2\" class=\"data row49 col2\" >$ 1391013.96</td>\n",
       "      <td id=\"T_7b638_row49_col3\" class=\"data row49 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row49_col4\" class=\"data row49 col4\" >35.40º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_7b638_row50_col0\" class=\"data row50 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row50_col1\" class=\"data row50 col1\" >21/01/11</td>\n",
       "      <td id=\"T_7b638_row50_col2\" class=\"data row50 col2\" >$ 1327405.42</td>\n",
       "      <td id=\"T_7b638_row50_col3\" class=\"data row50 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row50_col4\" class=\"data row50 col4\" >44.04º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_7b638_row51_col0\" class=\"data row51 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row51_col1\" class=\"data row51 col1\" >28/01/11</td>\n",
       "      <td id=\"T_7b638_row51_col2\" class=\"data row51 col2\" >$ 1316899.31</td>\n",
       "      <td id=\"T_7b638_row51_col3\" class=\"data row51 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row51_col4\" class=\"data row51 col4\" >43.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_7b638_row52_col0\" class=\"data row52 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row52_col1\" class=\"data row52 col1\" >02/04/11</td>\n",
       "      <td id=\"T_7b638_row52_col2\" class=\"data row52 col2\" >$ 1606629.58</td>\n",
       "      <td id=\"T_7b638_row52_col3\" class=\"data row52 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row52_col4\" class=\"data row52 col4\" >42.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_7b638_row53_col0\" class=\"data row53 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row53_col1\" class=\"data row53 col1\" >02/11/11</td>\n",
       "      <td id=\"T_7b638_row53_col2\" class=\"data row53 col2\" >$ 1649614.93</td>\n",
       "      <td id=\"T_7b638_row53_col3\" class=\"data row53 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row53_col4\" class=\"data row53 col4\" >36.39º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_7b638_row54_col0\" class=\"data row54 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row54_col1\" class=\"data row54 col1\" >18/02/11</td>\n",
       "      <td id=\"T_7b638_row54_col2\" class=\"data row54 col2\" >$ 1686842.78</td>\n",
       "      <td id=\"T_7b638_row54_col3\" class=\"data row54 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row54_col4\" class=\"data row54 col4\" >57.36º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_7b638_row55_col0\" class=\"data row55 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row55_col1\" class=\"data row55 col1\" >25/02/11</td>\n",
       "      <td id=\"T_7b638_row55_col2\" class=\"data row55 col2\" >$ 1456800.28</td>\n",
       "      <td id=\"T_7b638_row55_col3\" class=\"data row55 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row55_col4\" class=\"data row55 col4\" >62.90º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_7b638_row56_col0\" class=\"data row56 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row56_col1\" class=\"data row56 col1\" >03/04/11</td>\n",
       "      <td id=\"T_7b638_row56_col2\" class=\"data row56 col2\" >$ 1636263.41</td>\n",
       "      <td id=\"T_7b638_row56_col3\" class=\"data row56 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row56_col4\" class=\"data row56 col4\" >59.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_7b638_row57_col0\" class=\"data row57 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row57_col1\" class=\"data row57 col1\" >03/11/11</td>\n",
       "      <td id=\"T_7b638_row57_col2\" class=\"data row57 col2\" >$ 1553191.63</td>\n",
       "      <td id=\"T_7b638_row57_col3\" class=\"data row57 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row57_col4\" class=\"data row57 col4\" >53.56º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_7b638_row58_col0\" class=\"data row58 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row58_col1\" class=\"data row58 col1\" >18/03/11</td>\n",
       "      <td id=\"T_7b638_row58_col2\" class=\"data row58 col2\" >$ 1576818.06</td>\n",
       "      <td id=\"T_7b638_row58_col3\" class=\"data row58 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row58_col4\" class=\"data row58 col4\" >62.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_7b638_row59_col0\" class=\"data row59 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row59_col1\" class=\"data row59 col1\" >25/03/11</td>\n",
       "      <td id=\"T_7b638_row59_col2\" class=\"data row59 col2\" >$ 1541102.38</td>\n",
       "      <td id=\"T_7b638_row59_col3\" class=\"data row59 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row59_col4\" class=\"data row59 col4\" >69.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row60\" class=\"row_heading level0 row60\" >60</th>\n",
       "      <td id=\"T_7b638_row60_col0\" class=\"data row60 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row60_col1\" class=\"data row60 col1\" >04/01/11</td>\n",
       "      <td id=\"T_7b638_row60_col2\" class=\"data row60 col2\" >$ 1495064.75</td>\n",
       "      <td id=\"T_7b638_row60_col3\" class=\"data row60 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row60_col4\" class=\"data row60 col4\" >59.17º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row61\" class=\"row_heading level0 row61\" >61</th>\n",
       "      <td id=\"T_7b638_row61_col0\" class=\"data row61 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row61_col1\" class=\"data row61 col1\" >04/08/11</td>\n",
       "      <td id=\"T_7b638_row61_col2\" class=\"data row61 col2\" >$ 1614259.35</td>\n",
       "      <td id=\"T_7b638_row61_col3\" class=\"data row61 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row61_col4\" class=\"data row61 col4\" >67.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row62\" class=\"row_heading level0 row62\" >62</th>\n",
       "      <td id=\"T_7b638_row62_col0\" class=\"data row62 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row62_col1\" class=\"data row62 col1\" >15/04/11</td>\n",
       "      <td id=\"T_7b638_row62_col2\" class=\"data row62 col2\" >$ 1559889.00</td>\n",
       "      <td id=\"T_7b638_row62_col3\" class=\"data row62 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row62_col4\" class=\"data row62 col4\" >71.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row63\" class=\"row_heading level0 row63\" >63</th>\n",
       "      <td id=\"T_7b638_row63_col0\" class=\"data row63 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row63_col1\" class=\"data row63 col1\" >22/04/11</td>\n",
       "      <td id=\"T_7b638_row63_col2\" class=\"data row63 col2\" >$ 1564819.81</td>\n",
       "      <td id=\"T_7b638_row63_col3\" class=\"data row63 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row63_col4\" class=\"data row63 col4\" >72.99º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row64\" class=\"row_heading level0 row64\" >64</th>\n",
       "      <td id=\"T_7b638_row64_col0\" class=\"data row64 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row64_col1\" class=\"data row64 col1\" >29/04/11</td>\n",
       "      <td id=\"T_7b638_row64_col2\" class=\"data row64 col2\" >$ 1455090.69</td>\n",
       "      <td id=\"T_7b638_row64_col3\" class=\"data row64 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row64_col4\" class=\"data row64 col4\" >72.03º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row65\" class=\"row_heading level0 row65\" >65</th>\n",
       "      <td id=\"T_7b638_row65_col0\" class=\"data row65 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row65_col1\" class=\"data row65 col1\" >05/06/11</td>\n",
       "      <td id=\"T_7b638_row65_col2\" class=\"data row65 col2\" >$ 1629391.28</td>\n",
       "      <td id=\"T_7b638_row65_col3\" class=\"data row65 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row65_col4\" class=\"data row65 col4\" >64.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row66\" class=\"row_heading level0 row66\" >66</th>\n",
       "      <td id=\"T_7b638_row66_col0\" class=\"data row66 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row66_col1\" class=\"data row66 col1\" >13/05/11</td>\n",
       "      <td id=\"T_7b638_row66_col2\" class=\"data row66 col2\" >$ 1604775.58</td>\n",
       "      <td id=\"T_7b638_row66_col3\" class=\"data row66 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row66_col4\" class=\"data row66 col4\" >75.64º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row67\" class=\"row_heading level0 row67\" >67</th>\n",
       "      <td id=\"T_7b638_row67_col0\" class=\"data row67 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row67_col1\" class=\"data row67 col1\" >20/05/11</td>\n",
       "      <td id=\"T_7b638_row67_col2\" class=\"data row67 col2\" >$ 1428218.27</td>\n",
       "      <td id=\"T_7b638_row67_col3\" class=\"data row67 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row67_col4\" class=\"data row67 col4\" >67.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row68\" class=\"row_heading level0 row68\" >68</th>\n",
       "      <td id=\"T_7b638_row68_col0\" class=\"data row68 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row68_col1\" class=\"data row68 col1\" >27/05/11</td>\n",
       "      <td id=\"T_7b638_row68_col2\" class=\"data row68 col2\" >$ 1466046.67</td>\n",
       "      <td id=\"T_7b638_row68_col3\" class=\"data row68 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row68_col4\" class=\"data row68 col4\" >77.72º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row69\" class=\"row_heading level0 row69\" >69</th>\n",
       "      <td id=\"T_7b638_row69_col0\" class=\"data row69 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row69_col1\" class=\"data row69 col1\" >06/03/11</td>\n",
       "      <td id=\"T_7b638_row69_col2\" class=\"data row69 col2\" >$ 1635078.41</td>\n",
       "      <td id=\"T_7b638_row69_col3\" class=\"data row69 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row69_col4\" class=\"data row69 col4\" >83.00º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row70\" class=\"row_heading level0 row70\" >70</th>\n",
       "      <td id=\"T_7b638_row70_col0\" class=\"data row70 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row70_col1\" class=\"data row70 col1\" >06/10/11</td>\n",
       "      <td id=\"T_7b638_row70_col2\" class=\"data row70 col2\" >$ 1588948.32</td>\n",
       "      <td id=\"T_7b638_row70_col3\" class=\"data row70 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row70_col4\" class=\"data row70 col4\" >83.13º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row71\" class=\"row_heading level0 row71\" >71</th>\n",
       "      <td id=\"T_7b638_row71_col0\" class=\"data row71 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row71_col1\" class=\"data row71 col1\" >17/06/11</td>\n",
       "      <td id=\"T_7b638_row71_col2\" class=\"data row71 col2\" >$ 1532114.86</td>\n",
       "      <td id=\"T_7b638_row71_col3\" class=\"data row71 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row71_col4\" class=\"data row71 col4\" >86.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row72\" class=\"row_heading level0 row72\" >72</th>\n",
       "      <td id=\"T_7b638_row72_col0\" class=\"data row72 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row72_col1\" class=\"data row72 col1\" >24/06/11</td>\n",
       "      <td id=\"T_7b638_row72_col2\" class=\"data row72 col2\" >$ 1438830.15</td>\n",
       "      <td id=\"T_7b638_row72_col3\" class=\"data row72 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row72_col4\" class=\"data row72 col4\" >83.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row73\" class=\"row_heading level0 row73\" >73</th>\n",
       "      <td id=\"T_7b638_row73_col0\" class=\"data row73 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row73_col1\" class=\"data row73 col1\" >07/01/11</td>\n",
       "      <td id=\"T_7b638_row73_col2\" class=\"data row73 col2\" >$ 1488538.09</td>\n",
       "      <td id=\"T_7b638_row73_col3\" class=\"data row73 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row73_col4\" class=\"data row73 col4\" >85.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row74\" class=\"row_heading level0 row74\" >74</th>\n",
       "      <td id=\"T_7b638_row74_col0\" class=\"data row74 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row74_col1\" class=\"data row74 col1\" >07/08/11</td>\n",
       "      <td id=\"T_7b638_row74_col2\" class=\"data row74 col2\" >$ 1534849.64</td>\n",
       "      <td id=\"T_7b638_row74_col3\" class=\"data row74 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row74_col4\" class=\"data row74 col4\" >85.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row75\" class=\"row_heading level0 row75\" >75</th>\n",
       "      <td id=\"T_7b638_row75_col0\" class=\"data row75 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row75_col1\" class=\"data row75 col1\" >15/07/11</td>\n",
       "      <td id=\"T_7b638_row75_col2\" class=\"data row75 col2\" >$ 1455119.97</td>\n",
       "      <td id=\"T_7b638_row75_col3\" class=\"data row75 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row75_col4\" class=\"data row75 col4\" >88.54º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row76\" class=\"row_heading level0 row76\" >76</th>\n",
       "      <td id=\"T_7b638_row76_col0\" class=\"data row76 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row76_col1\" class=\"data row76 col1\" >22/07/11</td>\n",
       "      <td id=\"T_7b638_row76_col2\" class=\"data row76 col2\" >$ 1396926.82</td>\n",
       "      <td id=\"T_7b638_row76_col3\" class=\"data row76 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row76_col4\" class=\"data row76 col4\" >85.77º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row77\" class=\"row_heading level0 row77\" >77</th>\n",
       "      <td id=\"T_7b638_row77_col0\" class=\"data row77 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row77_col1\" class=\"data row77 col1\" >29/07/11</td>\n",
       "      <td id=\"T_7b638_row77_col2\" class=\"data row77 col2\" >$ 1352219.79</td>\n",
       "      <td id=\"T_7b638_row77_col3\" class=\"data row77 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row77_col4\" class=\"data row77 col4\" >86.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row78\" class=\"row_heading level0 row78\" >78</th>\n",
       "      <td id=\"T_7b638_row78_col0\" class=\"data row78 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row78_col1\" class=\"data row78 col1\" >08/05/11</td>\n",
       "      <td id=\"T_7b638_row78_col2\" class=\"data row78 col2\" >$ 1624383.75</td>\n",
       "      <td id=\"T_7b638_row78_col3\" class=\"data row78 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row78_col4\" class=\"data row78 col4\" >91.65º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row79\" class=\"row_heading level0 row79\" >79</th>\n",
       "      <td id=\"T_7b638_row79_col0\" class=\"data row79 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row79_col1\" class=\"data row79 col1\" >08/12/11</td>\n",
       "      <td id=\"T_7b638_row79_col2\" class=\"data row79 col2\" >$ 1525147.09</td>\n",
       "      <td id=\"T_7b638_row79_col3\" class=\"data row79 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row79_col4\" class=\"data row79 col4\" >90.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row80\" class=\"row_heading level0 row80\" >80</th>\n",
       "      <td id=\"T_7b638_row80_col0\" class=\"data row80 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row80_col1\" class=\"data row80 col1\" >19/08/11</td>\n",
       "      <td id=\"T_7b638_row80_col2\" class=\"data row80 col2\" >$ 1530761.43</td>\n",
       "      <td id=\"T_7b638_row80_col3\" class=\"data row80 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row80_col4\" class=\"data row80 col4\" >89.94º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row81\" class=\"row_heading level0 row81\" >81</th>\n",
       "      <td id=\"T_7b638_row81_col0\" class=\"data row81 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row81_col1\" class=\"data row81 col1\" >26/08/11</td>\n",
       "      <td id=\"T_7b638_row81_col2\" class=\"data row81 col2\" >$ 1464693.46</td>\n",
       "      <td id=\"T_7b638_row81_col3\" class=\"data row81 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row81_col4\" class=\"data row81 col4\" >87.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row82\" class=\"row_heading level0 row82\" >82</th>\n",
       "      <td id=\"T_7b638_row82_col0\" class=\"data row82 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row82_col1\" class=\"data row82 col1\" >09/02/11</td>\n",
       "      <td id=\"T_7b638_row82_col2\" class=\"data row82 col2\" >$ 1550229.22</td>\n",
       "      <td id=\"T_7b638_row82_col3\" class=\"data row82 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row82_col4\" class=\"data row82 col4\" >87.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row83\" class=\"row_heading level0 row83\" >83</th>\n",
       "      <td id=\"T_7b638_row83_col0\" class=\"data row83 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row83_col1\" class=\"data row83 col1\" >09/09/11</td>\n",
       "      <td id=\"T_7b638_row83_col2\" class=\"data row83 col2\" >$ 1540471.24</td>\n",
       "      <td id=\"T_7b638_row83_col3\" class=\"data row83 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row83_col4\" class=\"data row83 col4\" >76.00º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row84\" class=\"row_heading level0 row84\" >84</th>\n",
       "      <td id=\"T_7b638_row84_col0\" class=\"data row84 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row84_col1\" class=\"data row84 col1\" >16/09/11</td>\n",
       "      <td id=\"T_7b638_row84_col2\" class=\"data row84 col2\" >$ 1514259.78</td>\n",
       "      <td id=\"T_7b638_row84_col3\" class=\"data row84 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row84_col4\" class=\"data row84 col4\" >79.94º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row85\" class=\"row_heading level0 row85\" >85</th>\n",
       "      <td id=\"T_7b638_row85_col0\" class=\"data row85 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row85_col1\" class=\"data row85 col1\" >23/09/11</td>\n",
       "      <td id=\"T_7b638_row85_col2\" class=\"data row85 col2\" >$ 1380020.27</td>\n",
       "      <td id=\"T_7b638_row85_col3\" class=\"data row85 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row85_col4\" class=\"data row85 col4\" >75.80º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row86\" class=\"row_heading level0 row86\" >86</th>\n",
       "      <td id=\"T_7b638_row86_col0\" class=\"data row86 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row86_col1\" class=\"data row86 col1\" >30/09/11</td>\n",
       "      <td id=\"T_7b638_row86_col2\" class=\"data row86 col2\" >$ 1394561.83</td>\n",
       "      <td id=\"T_7b638_row86_col3\" class=\"data row86 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row86_col4\" class=\"data row86 col4\" >79.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row87\" class=\"row_heading level0 row87\" >87</th>\n",
       "      <td id=\"T_7b638_row87_col0\" class=\"data row87 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row87_col1\" class=\"data row87 col1\" >10/07/11</td>\n",
       "      <td id=\"T_7b638_row87_col2\" class=\"data row87 col2\" >$ 1630989.95</td>\n",
       "      <td id=\"T_7b638_row87_col3\" class=\"data row87 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row87_col4\" class=\"data row87 col4\" >69.31º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row88\" class=\"row_heading level0 row88\" >88</th>\n",
       "      <td id=\"T_7b638_row88_col0\" class=\"data row88 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row88_col1\" class=\"data row88 col1\" >14/10/11</td>\n",
       "      <td id=\"T_7b638_row88_col2\" class=\"data row88 col2\" >$ 1493525.93</td>\n",
       "      <td id=\"T_7b638_row88_col3\" class=\"data row88 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row88_col4\" class=\"data row88 col4\" >71.74º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row89\" class=\"row_heading level0 row89\" >89</th>\n",
       "      <td id=\"T_7b638_row89_col0\" class=\"data row89 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row89_col1\" class=\"data row89 col1\" >21/10/11</td>\n",
       "      <td id=\"T_7b638_row89_col2\" class=\"data row89 col2\" >$ 1502562.78</td>\n",
       "      <td id=\"T_7b638_row89_col3\" class=\"data row89 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row89_col4\" class=\"data row89 col4\" >63.71º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row90\" class=\"row_heading level0 row90\" >90</th>\n",
       "      <td id=\"T_7b638_row90_col0\" class=\"data row90 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row90_col1\" class=\"data row90 col1\" >28/10/11</td>\n",
       "      <td id=\"T_7b638_row90_col2\" class=\"data row90 col2\" >$ 1445249.09</td>\n",
       "      <td id=\"T_7b638_row90_col3\" class=\"data row90 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row90_col4\" class=\"data row90 col4\" >66.57º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row91\" class=\"row_heading level0 row91\" >91</th>\n",
       "      <td id=\"T_7b638_row91_col0\" class=\"data row91 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row91_col1\" class=\"data row91 col1\" >11/04/11</td>\n",
       "      <td id=\"T_7b638_row91_col2\" class=\"data row91 col2\" >$ 1697229.58</td>\n",
       "      <td id=\"T_7b638_row91_col3\" class=\"data row91 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row91_col4\" class=\"data row91 col4\" >54.98º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row92\" class=\"row_heading level0 row92\" >92</th>\n",
       "      <td id=\"T_7b638_row92_col0\" class=\"data row92 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row92_col1\" class=\"data row92 col1\" >11/11/11</td>\n",
       "      <td id=\"T_7b638_row92_col2\" class=\"data row92 col2\" >$ 1594938.89</td>\n",
       "      <td id=\"T_7b638_row92_col3\" class=\"data row92 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row92_col4\" class=\"data row92 col4\" >59.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row93\" class=\"row_heading level0 row93\" >93</th>\n",
       "      <td id=\"T_7b638_row93_col0\" class=\"data row93 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row93_col1\" class=\"data row93 col1\" >18/11/11</td>\n",
       "      <td id=\"T_7b638_row93_col2\" class=\"data row93 col2\" >$ 1539483.70</td>\n",
       "      <td id=\"T_7b638_row93_col3\" class=\"data row93 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row93_col4\" class=\"data row93 col4\" >62.25º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row94\" class=\"row_heading level0 row94\" >94</th>\n",
       "      <td id=\"T_7b638_row94_col0\" class=\"data row94 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row94_col1\" class=\"data row94 col1\" >25/11/11</td>\n",
       "      <td id=\"T_7b638_row94_col2\" class=\"data row94 col2\" >$ 2033320.66</td>\n",
       "      <td id=\"T_7b638_row94_col3\" class=\"data row94 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row94_col4\" class=\"data row94 col4\" >60.14º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row95\" class=\"row_heading level0 row95\" >95</th>\n",
       "      <td id=\"T_7b638_row95_col0\" class=\"data row95 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row95_col1\" class=\"data row95 col1\" >12/02/11</td>\n",
       "      <td id=\"T_7b638_row95_col2\" class=\"data row95 col2\" >$ 1584083.95</td>\n",
       "      <td id=\"T_7b638_row95_col3\" class=\"data row95 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row95_col4\" class=\"data row95 col4\" >48.91º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row96\" class=\"row_heading level0 row96\" >96</th>\n",
       "      <td id=\"T_7b638_row96_col0\" class=\"data row96 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row96_col1\" class=\"data row96 col1\" >12/09/11</td>\n",
       "      <td id=\"T_7b638_row96_col2\" class=\"data row96 col2\" >$ 1799682.38</td>\n",
       "      <td id=\"T_7b638_row96_col3\" class=\"data row96 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row96_col4\" class=\"data row96 col4\" >43.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row97\" class=\"row_heading level0 row97\" >97</th>\n",
       "      <td id=\"T_7b638_row97_col0\" class=\"data row97 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row97_col1\" class=\"data row97 col1\" >16/12/11</td>\n",
       "      <td id=\"T_7b638_row97_col2\" class=\"data row97 col2\" >$ 1881176.67</td>\n",
       "      <td id=\"T_7b638_row97_col3\" class=\"data row97 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row97_col4\" class=\"data row97 col4\" >51.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row98\" class=\"row_heading level0 row98\" >98</th>\n",
       "      <td id=\"T_7b638_row98_col0\" class=\"data row98 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row98_col1\" class=\"data row98 col1\" >23/12/11</td>\n",
       "      <td id=\"T_7b638_row98_col2\" class=\"data row98 col2\" >$ 2270188.99</td>\n",
       "      <td id=\"T_7b638_row98_col3\" class=\"data row98 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row98_col4\" class=\"data row98 col4\" >47.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row99\" class=\"row_heading level0 row99\" >99</th>\n",
       "      <td id=\"T_7b638_row99_col0\" class=\"data row99 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row99_col1\" class=\"data row99 col1\" >30/12/11</td>\n",
       "      <td id=\"T_7b638_row99_col2\" class=\"data row99 col2\" >$ 1497462.72</td>\n",
       "      <td id=\"T_7b638_row99_col3\" class=\"data row99 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row99_col4\" class=\"data row99 col4\" >44.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row100\" class=\"row_heading level0 row100\" >100</th>\n",
       "      <td id=\"T_7b638_row100_col0\" class=\"data row100 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row100_col1\" class=\"data row100 col1\" >01/06/12</td>\n",
       "      <td id=\"T_7b638_row100_col2\" class=\"data row100 col2\" >$ 1550369.92</td>\n",
       "      <td id=\"T_7b638_row100_col3\" class=\"data row100 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row100_col4\" class=\"data row100 col4\" >49.01º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row101\" class=\"row_heading level0 row101\" >101</th>\n",
       "      <td id=\"T_7b638_row101_col0\" class=\"data row101 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row101_col1\" class=\"data row101 col1\" >13/01/12</td>\n",
       "      <td id=\"T_7b638_row101_col2\" class=\"data row101 col2\" >$ 1459601.17</td>\n",
       "      <td id=\"T_7b638_row101_col3\" class=\"data row101 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row101_col4\" class=\"data row101 col4\" >48.53º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row102\" class=\"row_heading level0 row102\" >102</th>\n",
       "      <td id=\"T_7b638_row102_col0\" class=\"data row102 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row102_col1\" class=\"data row102 col1\" >20/01/12</td>\n",
       "      <td id=\"T_7b638_row102_col2\" class=\"data row102 col2\" >$ 1394393.84</td>\n",
       "      <td id=\"T_7b638_row102_col3\" class=\"data row102 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row102_col4\" class=\"data row102 col4\" >54.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row103\" class=\"row_heading level0 row103\" >103</th>\n",
       "      <td id=\"T_7b638_row103_col0\" class=\"data row103 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row103_col1\" class=\"data row103 col1\" >27/01/12</td>\n",
       "      <td id=\"T_7b638_row103_col2\" class=\"data row103 col2\" >$ 1319325.59</td>\n",
       "      <td id=\"T_7b638_row103_col3\" class=\"data row103 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row103_col4\" class=\"data row103 col4\" >54.26º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row104\" class=\"row_heading level0 row104\" >104</th>\n",
       "      <td id=\"T_7b638_row104_col0\" class=\"data row104 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row104_col1\" class=\"data row104 col1\" >02/03/12</td>\n",
       "      <td id=\"T_7b638_row104_col2\" class=\"data row104 col2\" >$ 1636339.65</td>\n",
       "      <td id=\"T_7b638_row104_col3\" class=\"data row104 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row104_col4\" class=\"data row104 col4\" >56.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row105\" class=\"row_heading level0 row105\" >105</th>\n",
       "      <td id=\"T_7b638_row105_col0\" class=\"data row105 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row105_col1\" class=\"data row105 col1\" >02/10/12</td>\n",
       "      <td id=\"T_7b638_row105_col2\" class=\"data row105 col2\" >$ 1802477.43</td>\n",
       "      <td id=\"T_7b638_row105_col3\" class=\"data row105 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row105_col4\" class=\"data row105 col4\" >48.02º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row106\" class=\"row_heading level0 row106\" >106</th>\n",
       "      <td id=\"T_7b638_row106_col0\" class=\"data row106 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row106_col1\" class=\"data row106 col1\" >17/02/12</td>\n",
       "      <td id=\"T_7b638_row106_col2\" class=\"data row106 col2\" >$ 1819870.00</td>\n",
       "      <td id=\"T_7b638_row106_col3\" class=\"data row106 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row106_col4\" class=\"data row106 col4\" >45.32º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row107\" class=\"row_heading level0 row107\" >107</th>\n",
       "      <td id=\"T_7b638_row107_col0\" class=\"data row107 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row107_col1\" class=\"data row107 col1\" >24/02/12</td>\n",
       "      <td id=\"T_7b638_row107_col2\" class=\"data row107 col2\" >$ 1539387.83</td>\n",
       "      <td id=\"T_7b638_row107_col3\" class=\"data row107 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row107_col4\" class=\"data row107 col4\" >57.25º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row108\" class=\"row_heading level0 row108\" >108</th>\n",
       "      <td id=\"T_7b638_row108_col0\" class=\"data row108 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row108_col1\" class=\"data row108 col1\" >03/02/12</td>\n",
       "      <td id=\"T_7b638_row108_col2\" class=\"data row108 col2\" >$ 1688420.76</td>\n",
       "      <td id=\"T_7b638_row108_col3\" class=\"data row108 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row108_col4\" class=\"data row108 col4\" >60.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row109\" class=\"row_heading level0 row109\" >109</th>\n",
       "      <td id=\"T_7b638_row109_col0\" class=\"data row109 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row109_col1\" class=\"data row109 col1\" >03/09/12</td>\n",
       "      <td id=\"T_7b638_row109_col2\" class=\"data row109 col2\" >$ 1675431.16</td>\n",
       "      <td id=\"T_7b638_row109_col3\" class=\"data row109 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row109_col4\" class=\"data row109 col4\" >58.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row110\" class=\"row_heading level0 row110\" >110</th>\n",
       "      <td id=\"T_7b638_row110_col0\" class=\"data row110 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row110_col1\" class=\"data row110 col1\" >16/03/12</td>\n",
       "      <td id=\"T_7b638_row110_col2\" class=\"data row110 col2\" >$ 1677472.78</td>\n",
       "      <td id=\"T_7b638_row110_col3\" class=\"data row110 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row110_col4\" class=\"data row110 col4\" >64.74º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row111\" class=\"row_heading level0 row111\" >111</th>\n",
       "      <td id=\"T_7b638_row111_col0\" class=\"data row111 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row111_col1\" class=\"data row111 col1\" >23/03/12</td>\n",
       "      <td id=\"T_7b638_row111_col2\" class=\"data row111 col2\" >$ 1511068.07</td>\n",
       "      <td id=\"T_7b638_row111_col3\" class=\"data row111 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row111_col4\" class=\"data row111 col4\" >65.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row112\" class=\"row_heading level0 row112\" >112</th>\n",
       "      <td id=\"T_7b638_row112_col0\" class=\"data row112 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row112_col1\" class=\"data row112 col1\" >30/03/12</td>\n",
       "      <td id=\"T_7b638_row112_col2\" class=\"data row112 col2\" >$ 1649604.63</td>\n",
       "      <td id=\"T_7b638_row112_col3\" class=\"data row112 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row112_col4\" class=\"data row112 col4\" >67.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row113\" class=\"row_heading level0 row113\" >113</th>\n",
       "      <td id=\"T_7b638_row113_col0\" class=\"data row113 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row113_col1\" class=\"data row113 col1\" >04/06/12</td>\n",
       "      <td id=\"T_7b638_row113_col2\" class=\"data row113 col2\" >$ 1899676.88</td>\n",
       "      <td id=\"T_7b638_row113_col3\" class=\"data row113 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row113_col4\" class=\"data row113 col4\" >70.43º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row114\" class=\"row_heading level0 row114\" >114</th>\n",
       "      <td id=\"T_7b638_row114_col0\" class=\"data row114 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row114_col1\" class=\"data row114 col1\" >13/04/12</td>\n",
       "      <td id=\"T_7b638_row114_col2\" class=\"data row114 col2\" >$ 1621031.70</td>\n",
       "      <td id=\"T_7b638_row114_col3\" class=\"data row114 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row114_col4\" class=\"data row114 col4\" >69.07º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row115\" class=\"row_heading level0 row115\" >115</th>\n",
       "      <td id=\"T_7b638_row115_col0\" class=\"data row115 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row115_col1\" class=\"data row115 col1\" >20/04/12</td>\n",
       "      <td id=\"T_7b638_row115_col2\" class=\"data row115 col2\" >$ 1521577.87</td>\n",
       "      <td id=\"T_7b638_row115_col3\" class=\"data row115 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row115_col4\" class=\"data row115 col4\" >66.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row116\" class=\"row_heading level0 row116\" >116</th>\n",
       "      <td id=\"T_7b638_row116_col0\" class=\"data row116 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row116_col1\" class=\"data row116 col1\" >27/04/12</td>\n",
       "      <td id=\"T_7b638_row116_col2\" class=\"data row116 col2\" >$ 1468928.37</td>\n",
       "      <td id=\"T_7b638_row116_col3\" class=\"data row116 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row116_col4\" class=\"data row116 col4\" >67.23º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row117\" class=\"row_heading level0 row117\" >117</th>\n",
       "      <td id=\"T_7b638_row117_col0\" class=\"data row117 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row117_col1\" class=\"data row117 col1\" >05/04/12</td>\n",
       "      <td id=\"T_7b638_row117_col2\" class=\"data row117 col2\" >$ 1684519.99</td>\n",
       "      <td id=\"T_7b638_row117_col3\" class=\"data row117 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row117_col4\" class=\"data row117 col4\" >75.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row118\" class=\"row_heading level0 row118\" >118</th>\n",
       "      <td id=\"T_7b638_row118_col0\" class=\"data row118 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row118_col1\" class=\"data row118 col1\" >05/11/12</td>\n",
       "      <td id=\"T_7b638_row118_col2\" class=\"data row118 col2\" >$ 1611096.05</td>\n",
       "      <td id=\"T_7b638_row118_col3\" class=\"data row118 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row118_col4\" class=\"data row118 col4\" >73.77º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row119\" class=\"row_heading level0 row119\" >119</th>\n",
       "      <td id=\"T_7b638_row119_col0\" class=\"data row119 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row119_col1\" class=\"data row119 col1\" >18/05/12</td>\n",
       "      <td id=\"T_7b638_row119_col2\" class=\"data row119 col2\" >$ 1595901.87</td>\n",
       "      <td id=\"T_7b638_row119_col3\" class=\"data row119 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row119_col4\" class=\"data row119 col4\" >70.33º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row120\" class=\"row_heading level0 row120\" >120</th>\n",
       "      <td id=\"T_7b638_row120_col0\" class=\"data row120 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row120_col1\" class=\"data row120 col1\" >25/05/12</td>\n",
       "      <td id=\"T_7b638_row120_col2\" class=\"data row120 col2\" >$ 1555444.55</td>\n",
       "      <td id=\"T_7b638_row120_col3\" class=\"data row120 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row120_col4\" class=\"data row120 col4\" >77.22º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row121\" class=\"row_heading level0 row121\" >121</th>\n",
       "      <td id=\"T_7b638_row121_col0\" class=\"data row121 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row121_col1\" class=\"data row121 col1\" >06/01/12</td>\n",
       "      <td id=\"T_7b638_row121_col2\" class=\"data row121 col2\" >$ 1624477.58</td>\n",
       "      <td id=\"T_7b638_row121_col3\" class=\"data row121 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row121_col4\" class=\"data row121 col4\" >77.95º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row122\" class=\"row_heading level0 row122\" >122</th>\n",
       "      <td id=\"T_7b638_row122_col0\" class=\"data row122 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row122_col1\" class=\"data row122 col1\" >06/08/12</td>\n",
       "      <td id=\"T_7b638_row122_col2\" class=\"data row122 col2\" >$ 1697230.96</td>\n",
       "      <td id=\"T_7b638_row122_col3\" class=\"data row122 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row122_col4\" class=\"data row122 col4\" >78.30º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row123\" class=\"row_heading level0 row123\" >123</th>\n",
       "      <td id=\"T_7b638_row123_col0\" class=\"data row123 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row123_col1\" class=\"data row123 col1\" >15/06/12</td>\n",
       "      <td id=\"T_7b638_row123_col2\" class=\"data row123 col2\" >$ 1630607.00</td>\n",
       "      <td id=\"T_7b638_row123_col3\" class=\"data row123 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row123_col4\" class=\"data row123 col4\" >79.35º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row124\" class=\"row_heading level0 row124\" >124</th>\n",
       "      <td id=\"T_7b638_row124_col0\" class=\"data row124 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row124_col1\" class=\"data row124 col1\" >22/06/12</td>\n",
       "      <td id=\"T_7b638_row124_col2\" class=\"data row124 col2\" >$ 1527845.81</td>\n",
       "      <td id=\"T_7b638_row124_col3\" class=\"data row124 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row124_col4\" class=\"data row124 col4\" >78.39º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row125\" class=\"row_heading level0 row125\" >125</th>\n",
       "      <td id=\"T_7b638_row125_col0\" class=\"data row125 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row125_col1\" class=\"data row125 col1\" >29/06/12</td>\n",
       "      <td id=\"T_7b638_row125_col2\" class=\"data row125 col2\" >$ 1540421.49</td>\n",
       "      <td id=\"T_7b638_row125_col3\" class=\"data row125 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row125_col4\" class=\"data row125 col4\" >84.88º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row126\" class=\"row_heading level0 row126\" >126</th>\n",
       "      <td id=\"T_7b638_row126_col0\" class=\"data row126 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row126_col1\" class=\"data row126 col1\" >07/06/12</td>\n",
       "      <td id=\"T_7b638_row126_col2\" class=\"data row126 col2\" >$ 1769854.16</td>\n",
       "      <td id=\"T_7b638_row126_col3\" class=\"data row126 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row126_col4\" class=\"data row126 col4\" >81.57º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row127\" class=\"row_heading level0 row127\" >127</th>\n",
       "      <td id=\"T_7b638_row127_col0\" class=\"data row127 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row127_col1\" class=\"data row127 col1\" >13/07/12</td>\n",
       "      <td id=\"T_7b638_row127_col2\" class=\"data row127 col2\" >$ 1527014.04</td>\n",
       "      <td id=\"T_7b638_row127_col3\" class=\"data row127 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row127_col4\" class=\"data row127 col4\" >77.12º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row128\" class=\"row_heading level0 row128\" >128</th>\n",
       "      <td id=\"T_7b638_row128_col0\" class=\"data row128 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row128_col1\" class=\"data row128 col1\" >20/07/12</td>\n",
       "      <td id=\"T_7b638_row128_col2\" class=\"data row128 col2\" >$ 1497954.76</td>\n",
       "      <td id=\"T_7b638_row128_col3\" class=\"data row128 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row128_col4\" class=\"data row128 col4\" >80.42º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row129\" class=\"row_heading level0 row129\" >129</th>\n",
       "      <td id=\"T_7b638_row129_col0\" class=\"data row129 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row129_col1\" class=\"data row129 col1\" >27/07/12</td>\n",
       "      <td id=\"T_7b638_row129_col2\" class=\"data row129 col2\" >$ 1439123.71</td>\n",
       "      <td id=\"T_7b638_row129_col3\" class=\"data row129 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row129_col4\" class=\"data row129 col4\" >82.66º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row130\" class=\"row_heading level0 row130\" >130</th>\n",
       "      <td id=\"T_7b638_row130_col0\" class=\"data row130 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row130_col1\" class=\"data row130 col1\" >08/03/12</td>\n",
       "      <td id=\"T_7b638_row130_col2\" class=\"data row130 col2\" >$ 1631135.79</td>\n",
       "      <td id=\"T_7b638_row130_col3\" class=\"data row130 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row130_col4\" class=\"data row130 col4\" >86.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row131\" class=\"row_heading level0 row131\" >131</th>\n",
       "      <td id=\"T_7b638_row131_col0\" class=\"data row131 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row131_col1\" class=\"data row131 col1\" >08/10/12</td>\n",
       "      <td id=\"T_7b638_row131_col2\" class=\"data row131 col2\" >$ 1592409.97</td>\n",
       "      <td id=\"T_7b638_row131_col3\" class=\"data row131 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row131_col4\" class=\"data row131 col4\" >85.05º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row132\" class=\"row_heading level0 row132\" >132</th>\n",
       "      <td id=\"T_7b638_row132_col0\" class=\"data row132 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row132_col1\" class=\"data row132 col1\" >17/08/12</td>\n",
       "      <td id=\"T_7b638_row132_col2\" class=\"data row132 col2\" >$ 1597868.05</td>\n",
       "      <td id=\"T_7b638_row132_col3\" class=\"data row132 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row132_col4\" class=\"data row132 col4\" >84.85º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row133\" class=\"row_heading level0 row133\" >133</th>\n",
       "      <td id=\"T_7b638_row133_col0\" class=\"data row133 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row133_col1\" class=\"data row133 col1\" >24/08/12</td>\n",
       "      <td id=\"T_7b638_row133_col2\" class=\"data row133 col2\" >$ 1494122.38</td>\n",
       "      <td id=\"T_7b638_row133_col3\" class=\"data row133 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row133_col4\" class=\"data row133 col4\" >77.66º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row134\" class=\"row_heading level0 row134\" >134</th>\n",
       "      <td id=\"T_7b638_row134_col0\" class=\"data row134 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row134_col1\" class=\"data row134 col1\" >31/08/12</td>\n",
       "      <td id=\"T_7b638_row134_col2\" class=\"data row134 col2\" >$ 1582083.40</td>\n",
       "      <td id=\"T_7b638_row134_col3\" class=\"data row134 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row134_col4\" class=\"data row134 col4\" >80.49º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row135\" class=\"row_heading level0 row135\" >135</th>\n",
       "      <td id=\"T_7b638_row135_col0\" class=\"data row135 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row135_col1\" class=\"data row135 col1\" >09/07/12</td>\n",
       "      <td id=\"T_7b638_row135_col2\" class=\"data row135 col2\" >$ 1661767.33</td>\n",
       "      <td id=\"T_7b638_row135_col3\" class=\"data row135 col3\" >True</td>\n",
       "      <td id=\"T_7b638_row135_col4\" class=\"data row135 col4\" >83.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row136\" class=\"row_heading level0 row136\" >136</th>\n",
       "      <td id=\"T_7b638_row136_col0\" class=\"data row136 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row136_col1\" class=\"data row136 col1\" >14/09/12</td>\n",
       "      <td id=\"T_7b638_row136_col2\" class=\"data row136 col2\" >$ 1517428.87</td>\n",
       "      <td id=\"T_7b638_row136_col3\" class=\"data row136 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row136_col4\" class=\"data row136 col4\" >74.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row137\" class=\"row_heading level0 row137\" >137</th>\n",
       "      <td id=\"T_7b638_row137_col0\" class=\"data row137 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row137_col1\" class=\"data row137 col1\" >21/09/12</td>\n",
       "      <td id=\"T_7b638_row137_col2\" class=\"data row137 col2\" >$ 1506126.06</td>\n",
       "      <td id=\"T_7b638_row137_col3\" class=\"data row137 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row137_col4\" class=\"data row137 col4\" >69.87º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row138\" class=\"row_heading level0 row138\" >138</th>\n",
       "      <td id=\"T_7b638_row138_col0\" class=\"data row138 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row138_col1\" class=\"data row138 col1\" >28/09/12</td>\n",
       "      <td id=\"T_7b638_row138_col2\" class=\"data row138 col2\" >$ 1437059.26</td>\n",
       "      <td id=\"T_7b638_row138_col3\" class=\"data row138 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row138_col4\" class=\"data row138 col4\" >76.08º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row139\" class=\"row_heading level0 row139\" >139</th>\n",
       "      <td id=\"T_7b638_row139_col0\" class=\"data row139 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row139_col1\" class=\"data row139 col1\" >10/05/12</td>\n",
       "      <td id=\"T_7b638_row139_col2\" class=\"data row139 col2\" >$ 1670785.97</td>\n",
       "      <td id=\"T_7b638_row139_col3\" class=\"data row139 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row139_col4\" class=\"data row139 col4\" >68.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row140\" class=\"row_heading level0 row140\" >140</th>\n",
       "      <td id=\"T_7b638_row140_col0\" class=\"data row140 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row140_col1\" class=\"data row140 col1\" >10/12/12</td>\n",
       "      <td id=\"T_7b638_row140_col2\" class=\"data row140 col2\" >$ 1573072.81</td>\n",
       "      <td id=\"T_7b638_row140_col3\" class=\"data row140 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row140_col4\" class=\"data row140 col4\" >62.99º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row141\" class=\"row_heading level0 row141\" >141</th>\n",
       "      <td id=\"T_7b638_row141_col0\" class=\"data row141 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row141_col1\" class=\"data row141 col1\" >19/10/12</td>\n",
       "      <td id=\"T_7b638_row141_col2\" class=\"data row141 col2\" >$ 1508068.77</td>\n",
       "      <td id=\"T_7b638_row141_col3\" class=\"data row141 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row141_col4\" class=\"data row141 col4\" >67.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b638_level0_row142\" class=\"row_heading level0 row142\" >142</th>\n",
       "      <td id=\"T_7b638_row142_col0\" class=\"data row142 col0\" >1</td>\n",
       "      <td id=\"T_7b638_row142_col1\" class=\"data row142 col1\" >26/10/12</td>\n",
       "      <td id=\"T_7b638_row142_col2\" class=\"data row142 col2\" >$ 1493659.74</td>\n",
       "      <td id=\"T_7b638_row142_col3\" class=\"data row142 col3\" >False</td>\n",
       "      <td id=\"T_7b638_row142_col4\" class=\"data row142 col4\" >69.16º</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb51cf39670>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_dict = {\n",
    "    \"data\": \"{:%d/%m/%y}\",\n",
    "    \"vendas_semanais\": \"$ {:.2f}\",\n",
    "    \"temperatura\": \"{:.2f}º\"\n",
    "}\n",
    "df_aux.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8472f1",
   "metadata": {},
   "source": [
    "- Adicionando cores e barras para destacar valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "333783d5",
   "metadata": {},
   "outputs": [
    {
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       "#T_aa7d2_row25_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 94.6%, transparent 94.6%);\n",
       "}\n",
       "#T_aa7d2_row26_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.6%, transparent 97.6%);\n",
       "}\n",
       "#T_aa7d2_row27_col2, #T_aa7d2_row31_col2, #T_aa7d2_row35_col2, #T_aa7d2_row137_col2, #T_aa7d2_row141_col2 {\n",
       "  background-color: #d9f0d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row27_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.5%, transparent 97.5%);\n",
       "}\n",
       "#T_aa7d2_row28_col2, #T_aa7d2_row111_col2 {\n",
       "  background-color: #d8f0d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row28_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.3%, transparent 97.3%);\n",
       "}\n",
       "#T_aa7d2_row29_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 96.5%, transparent 96.5%);\n",
       "}\n",
       "#T_aa7d2_row30_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 94.3%, transparent 94.3%);\n",
       "}\n",
       "#T_aa7d2_row31_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.9%, transparent 92.9%);\n",
       "}\n",
       "#T_aa7d2_row32_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 94.8%, transparent 94.8%);\n",
       "}\n",
       "#T_aa7d2_row33_col2, #T_aa7d2_row77_col2 {\n",
       "  background-color: #f2faf0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row33_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 94.2%, transparent 94.2%);\n",
       "}\n",
       "#T_aa7d2_row34_col2 {\n",
       "  background-color: #e5f5e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row34_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 89.2%, transparent 89.2%);\n",
       "}\n",
       "#T_aa7d2_row35_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 84.9%, transparent 84.9%);\n",
       "}\n",
       "#T_aa7d2_row36_col2, #T_aa7d2_row101_col2 {\n",
       "  background-color: #e3f4de;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row36_col4, #T_aa7d2_row116_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.7%, transparent 86.7%);\n",
       "}\n",
       "#T_aa7d2_row37_col2 {\n",
       "  background-color: #f4fbf1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row37_col4, #T_aa7d2_row137_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 88.1%, transparent 88.1%);\n",
       "}\n",
       "#T_aa7d2_row38_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 88.0%, transparent 88.0%);\n",
       "}\n",
       "#T_aa7d2_row39_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.0%, transparent 82.0%);\n",
       "}\n",
       "#T_aa7d2_row40_col4, #T_aa7d2_row56_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.5%, transparent 82.5%);\n",
       "}\n",
       "#T_aa7d2_row41_col2 {\n",
       "  background-color: #def2d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row41_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 78.0%, transparent 78.0%);\n",
       "}\n",
       "#T_aa7d2_row42_col2 {\n",
       "  background-color: #4db163;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row42_col4, #T_aa7d2_row65_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 85.2%, transparent 85.2%);\n",
       "}\n",
       "#T_aa7d2_row43_col2, #T_aa7d2_row82_col2, #T_aa7d2_row100_col2 {\n",
       "  background-color: #cfecc9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row43_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.9%, transparent 76.9%);\n",
       "}\n",
       "#T_aa7d2_row44_col2, #T_aa7d2_row117_col2 {\n",
       "  background-color: #abdda5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row44_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 75.3%, transparent 75.3%);\n",
       "}\n",
       "#T_aa7d2_row45_col2 {\n",
       "  background-color: #65bd6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row45_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 77.2%, transparent 77.2%);\n",
       "}\n",
       "#T_aa7d2_row46_col2 {\n",
       "  background-color: #00441b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row46_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 78.5%, transparent 78.5%);\n",
       "}\n",
       "#T_aa7d2_row47_col2 {\n",
       "  background-color: #f0f9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row47_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.4%, transparent 76.4%);\n",
       "}\n",
       "#T_aa7d2_row48_col2, #T_aa7d2_row90_col2 {\n",
       "  background-color: #e6f5e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row48_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.3%, transparent 76.3%);\n",
       "}\n",
       "#T_aa7d2_row49_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 69.3%, transparent 69.3%);\n",
       "}\n",
       "#T_aa7d2_row50_col2 {\n",
       "  background-color: #f6fcf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row50_col4, #T_aa7d2_row96_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 74.0%, transparent 74.0%);\n",
       "}\n",
       "#T_aa7d2_row51_col2, #T_aa7d2_row103_col2 {\n",
       "  background-color: #f7fcf5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row51_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 73.9%, transparent 73.9%);\n",
       "}\n",
       "#T_aa7d2_row52_col2 {\n",
       "  background-color: #c1e6ba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row53_col2, #T_aa7d2_row112_col2 {\n",
       "  background-color: #b5e1ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row53_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 69.9%, transparent 69.9%);\n",
       "}\n",
       "#T_aa7d2_row54_col2, #T_aa7d2_row108_col2 {\n",
       "  background-color: #aadda4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row54_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 81.3%, transparent 81.3%);\n",
       "}\n",
       "#T_aa7d2_row55_col2, #T_aa7d2_row64_col2, #T_aa7d2_row75_col2 {\n",
       "  background-color: #e4f5df;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row55_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 84.3%, transparent 84.3%);\n",
       "}\n",
       "#T_aa7d2_row56_col2, #T_aa7d2_row69_col2, #T_aa7d2_row104_col2 {\n",
       "  background-color: #b8e3b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row57_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 79.2%, transparent 79.2%);\n",
       "}\n",
       "#T_aa7d2_row58_col2 {\n",
       "  background-color: #c9eac2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row58_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 84.2%, transparent 84.2%);\n",
       "}\n",
       "#T_aa7d2_row59_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 88.2%, transparent 88.2%);\n",
       "}\n",
       "#T_aa7d2_row60_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.3%, transparent 82.3%);\n",
       "}\n",
       "#T_aa7d2_row61_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 87.0%, transparent 87.0%);\n",
       "}\n",
       "#T_aa7d2_row62_col2 {\n",
       "  background-color: #ccebc6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row62_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 88.9%, transparent 88.9%);\n",
       "}\n",
       "#T_aa7d2_row63_col2 {\n",
       "  background-color: #cbebc5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row63_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 89.8%, transparent 89.8%);\n",
       "}\n",
       "#T_aa7d2_row64_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 89.3%, transparent 89.3%);\n",
       "}\n",
       "#T_aa7d2_row65_col2, #T_aa7d2_row123_col2 {\n",
       "  background-color: #bbe4b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row66_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 91.3%, transparent 91.3%);\n",
       "}\n",
       "#T_aa7d2_row67_col2 {\n",
       "  background-color: #e8f6e4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row67_col4, #T_aa7d2_row112_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.9%, transparent 86.9%);\n",
       "}\n",
       "#T_aa7d2_row68_col4, #T_aa7d2_row133_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.4%, transparent 92.4%);\n",
       "}\n",
       "#T_aa7d2_row69_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 95.3%, transparent 95.3%);\n",
       "}\n",
       "#T_aa7d2_row70_col2, #T_aa7d2_row131_col2 {\n",
       "  background-color: #c6e8bf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row71_col2, #T_aa7d2_row80_col2 {\n",
       "  background-color: #d3eecd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row71_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.1%, transparent 97.1%);\n",
       "}\n",
       "#T_aa7d2_row72_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 95.6%, transparent 95.6%);\n",
       "}\n",
       "#T_aa7d2_row73_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 96.7%, transparent 96.7%);\n",
       "}\n",
       "#T_aa7d2_row74_col2 {\n",
       "  background-color: #d2edcc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row74_col4, #T_aa7d2_row76_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 96.8%, transparent 96.8%);\n",
       "}\n",
       "#T_aa7d2_row75_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 98.3%, transparent 98.3%);\n",
       "}\n",
       "#T_aa7d2_row77_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.4%, transparent 97.4%);\n",
       "}\n",
       "#T_aa7d2_row78_col2, #T_aa7d2_row121_col2 {\n",
       "  background-color: #bce4b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row78_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_aa7d2_row79_col2 {\n",
       "  background-color: #d5efcf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row79_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 99.5%, transparent 99.5%);\n",
       "}\n",
       "#T_aa7d2_row80_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 99.1%, transparent 99.1%);\n",
       "}\n",
       "#T_aa7d2_row81_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 98.0%, transparent 98.0%);\n",
       "}\n",
       "#T_aa7d2_row82_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.9%, transparent 97.9%);\n",
       "}\n",
       "#T_aa7d2_row83_col4, #T_aa7d2_row138_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 91.5%, transparent 91.5%);\n",
       "}\n",
       "#T_aa7d2_row84_col2, #T_aa7d2_row136_col2 {\n",
       "  background-color: #d7efd1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row84_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 93.6%, transparent 93.6%);\n",
       "}\n",
       "#T_aa7d2_row85_col2 {\n",
       "  background-color: #eff9eb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row85_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 91.4%, transparent 91.4%);\n",
       "}\n",
       "#T_aa7d2_row86_col2, #T_aa7d2_row102_col2 {\n",
       "  background-color: #edf8e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row86_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 93.5%, transparent 93.5%);\n",
       "}\n",
       "#T_aa7d2_row87_col2, #T_aa7d2_row130_col2 {\n",
       "  background-color: #bae3b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row87_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 87.8%, transparent 87.8%);\n",
       "}\n",
       "#T_aa7d2_row88_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 89.1%, transparent 89.1%);\n",
       "}\n",
       "#T_aa7d2_row89_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 84.8%, transparent 84.8%);\n",
       "}\n",
       "#T_aa7d2_row90_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.3%, transparent 86.3%);\n",
       "}\n",
       "#T_aa7d2_row91_col2, #T_aa7d2_row122_col2 {\n",
       "  background-color: #a8dca2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row91_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 80.0%, transparent 80.0%);\n",
       "}\n",
       "#T_aa7d2_row92_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.2%, transparent 82.2%);\n",
       "}\n",
       "#T_aa7d2_row94_col2 {\n",
       "  background-color: #369f54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row94_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.8%, transparent 82.8%);\n",
       "}\n",
       "#T_aa7d2_row95_col2, #T_aa7d2_row134_col2 {\n",
       "  background-color: #c8e9c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row95_col4, #T_aa7d2_row100_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.7%, transparent 76.7%);\n",
       "}\n",
       "#T_aa7d2_row96_col2 {\n",
       "  background-color: #86cc85;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row97_col2 {\n",
       "  background-color: #6abf71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row97_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 78.2%, transparent 78.2%);\n",
       "}\n",
       "#T_aa7d2_row98_col2 {\n",
       "  background-color: #00682a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row98_col4, #T_aa7d2_row105_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.2%, transparent 76.2%);\n",
       "}\n",
       "#T_aa7d2_row99_col2, #T_aa7d2_row128_col2 {\n",
       "  background-color: #dbf1d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row99_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 74.3%, transparent 74.3%);\n",
       "}\n",
       "#T_aa7d2_row101_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 76.5%, transparent 76.5%);\n",
       "}\n",
       "#T_aa7d2_row102_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 79.5%, transparent 79.5%);\n",
       "}\n",
       "#T_aa7d2_row103_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 79.6%, transparent 79.6%);\n",
       "}\n",
       "#T_aa7d2_row104_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 80.9%, transparent 80.9%);\n",
       "}\n",
       "#T_aa7d2_row105_col2 {\n",
       "  background-color: #84cc83;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row106_col2 {\n",
       "  background-color: #7fc97f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row106_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 74.7%, transparent 74.7%);\n",
       "}\n",
       "#T_aa7d2_row107_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 81.2%, transparent 81.2%);\n",
       "}\n",
       "#T_aa7d2_row108_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 83.3%, transparent 83.3%);\n",
       "}\n",
       "#T_aa7d2_row109_col2 {\n",
       "  background-color: #aedea7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row109_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 82.1%, transparent 82.1%);\n",
       "}\n",
       "#T_aa7d2_row110_col2 {\n",
       "  background-color: #acdea6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row110_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 85.3%, transparent 85.3%);\n",
       "}\n",
       "#T_aa7d2_row111_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.0%, transparent 86.0%);\n",
       "}\n",
       "#T_aa7d2_row113_col2 {\n",
       "  background-color: #62bb6d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_aa7d2_row113_col4, #T_aa7d2_row119_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 88.4%, transparent 88.4%);\n",
       "}\n",
       "#T_aa7d2_row114_col2 {\n",
       "  background-color: #bde5b6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row114_col4, #T_aa7d2_row142_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 87.7%, transparent 87.7%);\n",
       "}\n",
       "#T_aa7d2_row115_col2 {\n",
       "  background-color: #d6efd0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row115_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 86.4%, transparent 86.4%);\n",
       "}\n",
       "#T_aa7d2_row116_col2 {\n",
       "  background-color: #e1f3dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row117_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 91.2%, transparent 91.2%);\n",
       "}\n",
       "#T_aa7d2_row118_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 90.2%, transparent 90.2%);\n",
       "}\n",
       "#T_aa7d2_row120_col2 {\n",
       "  background-color: #cdecc7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row120_col4, #T_aa7d2_row127_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.1%, transparent 92.1%);\n",
       "}\n",
       "#T_aa7d2_row121_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.5%, transparent 92.5%);\n",
       "}\n",
       "#T_aa7d2_row122_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.7%, transparent 92.7%);\n",
       "}\n",
       "#T_aa7d2_row123_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 93.3%, transparent 93.3%);\n",
       "}\n",
       "#T_aa7d2_row124_col2, #T_aa7d2_row127_col2 {\n",
       "  background-color: #d4eece;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row124_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 92.8%, transparent 92.8%);\n",
       "}\n",
       "#T_aa7d2_row125_col4, #T_aa7d2_row132_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 96.3%, transparent 96.3%);\n",
       "}\n",
       "#T_aa7d2_row126_col2 {\n",
       "  background-color: #90d18d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row126_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 94.5%, transparent 94.5%);\n",
       "}\n",
       "#T_aa7d2_row129_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 95.1%, transparent 95.1%);\n",
       "}\n",
       "#T_aa7d2_row130_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 97.0%, transparent 97.0%);\n",
       "}\n",
       "#T_aa7d2_row131_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 96.4%, transparent 96.4%);\n",
       "}\n",
       "#T_aa7d2_row132_col2 {\n",
       "  background-color: #c3e7bc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row135_col2 {\n",
       "  background-color: #b1e0ab;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row135_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 95.8%, transparent 95.8%);\n",
       "}\n",
       "#T_aa7d2_row136_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 90.9%, transparent 90.9%);\n",
       "}\n",
       "#T_aa7d2_row138_col2 {\n",
       "  background-color: #e7f6e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row139_col2 {\n",
       "  background-color: #afdfa8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row139_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 87.4%, transparent 87.4%);\n",
       "}\n",
       "#T_aa7d2_row140_col2 {\n",
       "  background-color: #caeac3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_aa7d2_row140_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 84.4%, transparent 84.4%);\n",
       "}\n",
       "#T_aa7d2_row141_col4 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 50.0%, lightblue 50.0%, lightblue 87.1%, transparent 87.1%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_aa7d2\">\n",
       "  <caption>Vendas semanais das lojas do Walmart</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_aa7d2_level0_col0\" class=\"col_heading level0 col0\" >loja</th>\n",
       "      <th id=\"T_aa7d2_level0_col1\" class=\"col_heading level0 col1\" >data</th>\n",
       "      <th id=\"T_aa7d2_level0_col2\" class=\"col_heading level0 col2\" >vendas_semanais</th>\n",
       "      <th id=\"T_aa7d2_level0_col3\" class=\"col_heading level0 col3\" >feriado</th>\n",
       "      <th id=\"T_aa7d2_level0_col4\" class=\"col_heading level0 col4\" >temperatura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row0_col1\" class=\"data row0 col1\" >02/05/10</td>\n",
       "      <td id=\"T_aa7d2_row0_col2\" class=\"data row0 col2\" >$ 1643690.90</td>\n",
       "      <td id=\"T_aa7d2_row0_col3\" class=\"data row0 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row0_col4\" class=\"data row0 col4\" >42.31º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row1_col1\" class=\"data row1 col1\" >02/12/10</td>\n",
       "      <td id=\"T_aa7d2_row1_col2\" class=\"data row1 col2\" >$ 1641957.44</td>\n",
       "      <td id=\"T_aa7d2_row1_col3\" class=\"data row1 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row1_col4\" class=\"data row1 col4\" >38.51º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row2_col1\" class=\"data row2 col1\" >19/02/10</td>\n",
       "      <td id=\"T_aa7d2_row2_col2\" class=\"data row2 col2\" >$ 1611968.17</td>\n",
       "      <td id=\"T_aa7d2_row2_col3\" class=\"data row2 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row2_col4\" class=\"data row2 col4\" >39.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row3_col0\" class=\"data row3 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row3_col1\" class=\"data row3 col1\" >26/02/10</td>\n",
       "      <td id=\"T_aa7d2_row3_col2\" class=\"data row3 col2\" >$ 1409727.59</td>\n",
       "      <td id=\"T_aa7d2_row3_col3\" class=\"data row3 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row3_col4\" class=\"data row3 col4\" >46.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row4_col1\" class=\"data row4 col1\" >03/05/10</td>\n",
       "      <td id=\"T_aa7d2_row4_col2\" class=\"data row4 col2\" >$ 1554806.68</td>\n",
       "      <td id=\"T_aa7d2_row4_col3\" class=\"data row4 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row4_col4\" class=\"data row4 col4\" >46.50º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row5_col1\" class=\"data row5 col1\" >03/12/10</td>\n",
       "      <td id=\"T_aa7d2_row5_col2\" class=\"data row5 col2\" >$ 1439541.59</td>\n",
       "      <td id=\"T_aa7d2_row5_col3\" class=\"data row5 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row5_col4\" class=\"data row5 col4\" >57.79º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row6_col0\" class=\"data row6 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row6_col1\" class=\"data row6 col1\" >19/03/10</td>\n",
       "      <td id=\"T_aa7d2_row6_col2\" class=\"data row6 col2\" >$ 1472515.79</td>\n",
       "      <td id=\"T_aa7d2_row6_col3\" class=\"data row6 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row6_col4\" class=\"data row6 col4\" >54.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row7_col0\" class=\"data row7 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row7_col1\" class=\"data row7 col1\" >26/03/10</td>\n",
       "      <td id=\"T_aa7d2_row7_col2\" class=\"data row7 col2\" >$ 1404429.92</td>\n",
       "      <td id=\"T_aa7d2_row7_col3\" class=\"data row7 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row7_col4\" class=\"data row7 col4\" >51.45º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row8_col0\" class=\"data row8 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row8_col1\" class=\"data row8 col1\" >04/02/10</td>\n",
       "      <td id=\"T_aa7d2_row8_col2\" class=\"data row8 col2\" >$ 1594968.28</td>\n",
       "      <td id=\"T_aa7d2_row8_col3\" class=\"data row8 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row8_col4\" class=\"data row8 col4\" >62.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row9_col0\" class=\"data row9 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row9_col1\" class=\"data row9 col1\" >04/09/10</td>\n",
       "      <td id=\"T_aa7d2_row9_col2\" class=\"data row9 col2\" >$ 1545418.53</td>\n",
       "      <td id=\"T_aa7d2_row9_col3\" class=\"data row9 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row9_col4\" class=\"data row9 col4\" >65.86º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row10_col0\" class=\"data row10 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row10_col1\" class=\"data row10 col1\" >16/04/10</td>\n",
       "      <td id=\"T_aa7d2_row10_col2\" class=\"data row10 col2\" >$ 1466058.28</td>\n",
       "      <td id=\"T_aa7d2_row10_col3\" class=\"data row10 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row10_col4\" class=\"data row10 col4\" >66.32º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row11_col0\" class=\"data row11 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row11_col1\" class=\"data row11 col1\" >23/04/10</td>\n",
       "      <td id=\"T_aa7d2_row11_col2\" class=\"data row11 col2\" >$ 1391256.12</td>\n",
       "      <td id=\"T_aa7d2_row11_col3\" class=\"data row11 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row11_col4\" class=\"data row11 col4\" >64.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row12_col0\" class=\"data row12 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row12_col1\" class=\"data row12 col1\" >30/04/10</td>\n",
       "      <td id=\"T_aa7d2_row12_col2\" class=\"data row12 col2\" >$ 1425100.71</td>\n",
       "      <td id=\"T_aa7d2_row12_col3\" class=\"data row12 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row12_col4\" class=\"data row12 col4\" >67.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row13_col0\" class=\"data row13 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row13_col1\" class=\"data row13 col1\" >05/07/10</td>\n",
       "      <td id=\"T_aa7d2_row13_col2\" class=\"data row13 col2\" >$ 1603955.12</td>\n",
       "      <td id=\"T_aa7d2_row13_col3\" class=\"data row13 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row13_col4\" class=\"data row13 col4\" >72.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row14_col0\" class=\"data row14 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row14_col1\" class=\"data row14 col1\" >14/05/10</td>\n",
       "      <td id=\"T_aa7d2_row14_col2\" class=\"data row14 col2\" >$ 1494251.50</td>\n",
       "      <td id=\"T_aa7d2_row14_col3\" class=\"data row14 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row14_col4\" class=\"data row14 col4\" >74.78º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row15_col0\" class=\"data row15 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row15_col1\" class=\"data row15 col1\" >21/05/10</td>\n",
       "      <td id=\"T_aa7d2_row15_col2\" class=\"data row15 col2\" >$ 1399662.07</td>\n",
       "      <td id=\"T_aa7d2_row15_col3\" class=\"data row15 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row15_col4\" class=\"data row15 col4\" >76.44º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row16_col0\" class=\"data row16 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row16_col1\" class=\"data row16 col1\" >28/05/10</td>\n",
       "      <td id=\"T_aa7d2_row16_col2\" class=\"data row16 col2\" >$ 1432069.95</td>\n",
       "      <td id=\"T_aa7d2_row16_col3\" class=\"data row16 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row16_col4\" class=\"data row16 col4\" >80.44º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row17_col0\" class=\"data row17 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row17_col1\" class=\"data row17 col1\" >06/04/10</td>\n",
       "      <td id=\"T_aa7d2_row17_col2\" class=\"data row17 col2\" >$ 1615524.71</td>\n",
       "      <td id=\"T_aa7d2_row17_col3\" class=\"data row17 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row17_col4\" class=\"data row17 col4\" >80.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row18_col0\" class=\"data row18 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row18_col1\" class=\"data row18 col1\" >06/11/10</td>\n",
       "      <td id=\"T_aa7d2_row18_col2\" class=\"data row18 col2\" >$ 1542561.09</td>\n",
       "      <td id=\"T_aa7d2_row18_col3\" class=\"data row18 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row18_col4\" class=\"data row18 col4\" >80.43º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row19_col0\" class=\"data row19 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row19_col1\" class=\"data row19 col1\" >18/06/10</td>\n",
       "      <td id=\"T_aa7d2_row19_col2\" class=\"data row19 col2\" >$ 1503284.06</td>\n",
       "      <td id=\"T_aa7d2_row19_col3\" class=\"data row19 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row19_col4\" class=\"data row19 col4\" >84.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row20_col0\" class=\"data row20 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row20_col1\" class=\"data row20 col1\" >25/06/10</td>\n",
       "      <td id=\"T_aa7d2_row20_col2\" class=\"data row20 col2\" >$ 1422711.60</td>\n",
       "      <td id=\"T_aa7d2_row20_col3\" class=\"data row20 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row20_col4\" class=\"data row20 col4\" >84.34º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row21_col0\" class=\"data row21 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row21_col1\" class=\"data row21 col1\" >07/02/10</td>\n",
       "      <td id=\"T_aa7d2_row21_col2\" class=\"data row21 col2\" >$ 1492418.14</td>\n",
       "      <td id=\"T_aa7d2_row21_col3\" class=\"data row21 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row21_col4\" class=\"data row21 col4\" >80.91º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row22_col0\" class=\"data row22 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row22_col1\" class=\"data row22 col1\" >07/09/10</td>\n",
       "      <td id=\"T_aa7d2_row22_col2\" class=\"data row22 col2\" >$ 1546074.18</td>\n",
       "      <td id=\"T_aa7d2_row22_col3\" class=\"data row22 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row22_col4\" class=\"data row22 col4\" >80.48º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row23_col0\" class=\"data row23 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row23_col1\" class=\"data row23 col1\" >16/07/10</td>\n",
       "      <td id=\"T_aa7d2_row23_col2\" class=\"data row23 col2\" >$ 1448938.92</td>\n",
       "      <td id=\"T_aa7d2_row23_col3\" class=\"data row23 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row23_col4\" class=\"data row23 col4\" >83.15º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row24_col0\" class=\"data row24 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row24_col1\" class=\"data row24 col1\" >23/07/10</td>\n",
       "      <td id=\"T_aa7d2_row24_col2\" class=\"data row24 col2\" >$ 1385065.20</td>\n",
       "      <td id=\"T_aa7d2_row24_col3\" class=\"data row24 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row24_col4\" class=\"data row24 col4\" >83.36º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row25_col0\" class=\"data row25 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row25_col1\" class=\"data row25 col1\" >30/07/10</td>\n",
       "      <td id=\"T_aa7d2_row25_col2\" class=\"data row25 col2\" >$ 1371986.60</td>\n",
       "      <td id=\"T_aa7d2_row25_col3\" class=\"data row25 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row25_col4\" class=\"data row25 col4\" >81.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row26_col0\" class=\"data row26 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row26_col1\" class=\"data row26 col1\" >08/06/10</td>\n",
       "      <td id=\"T_aa7d2_row26_col2\" class=\"data row26 col2\" >$ 1605491.78</td>\n",
       "      <td id=\"T_aa7d2_row26_col3\" class=\"data row26 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row26_col4\" class=\"data row26 col4\" >87.16º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row27_col0\" class=\"data row27 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row27_col1\" class=\"data row27 col1\" >13/08/10</td>\n",
       "      <td id=\"T_aa7d2_row27_col2\" class=\"data row27 col2\" >$ 1508237.76</td>\n",
       "      <td id=\"T_aa7d2_row27_col3\" class=\"data row27 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row27_col4\" class=\"data row27 col4\" >87.00º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row28_col0\" class=\"data row28 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row28_col1\" class=\"data row28 col1\" >20/08/10</td>\n",
       "      <td id=\"T_aa7d2_row28_col2\" class=\"data row28 col2\" >$ 1513080.49</td>\n",
       "      <td id=\"T_aa7d2_row28_col3\" class=\"data row28 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row28_col4\" class=\"data row28 col4\" >86.65º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row29_col0\" class=\"data row29 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row29_col1\" class=\"data row29 col1\" >27/08/10</td>\n",
       "      <td id=\"T_aa7d2_row29_col2\" class=\"data row29 col2\" >$ 1449142.92</td>\n",
       "      <td id=\"T_aa7d2_row29_col3\" class=\"data row29 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row29_col4\" class=\"data row29 col4\" >85.22º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row30_col0\" class=\"data row30 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row30_col1\" class=\"data row30 col1\" >09/03/10</td>\n",
       "      <td id=\"T_aa7d2_row30_col2\" class=\"data row30 col2\" >$ 1540163.53</td>\n",
       "      <td id=\"T_aa7d2_row30_col3\" class=\"data row30 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row30_col4\" class=\"data row30 col4\" >81.21º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row31_col0\" class=\"data row31 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row31_col1\" class=\"data row31 col1\" >09/10/10</td>\n",
       "      <td id=\"T_aa7d2_row31_col2\" class=\"data row31 col2\" >$ 1507460.69</td>\n",
       "      <td id=\"T_aa7d2_row31_col3\" class=\"data row31 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row31_col4\" class=\"data row31 col4\" >78.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row32_col0\" class=\"data row32 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row32_col1\" class=\"data row32 col1\" >17/09/10</td>\n",
       "      <td id=\"T_aa7d2_row32_col2\" class=\"data row32 col2\" >$ 1430378.67</td>\n",
       "      <td id=\"T_aa7d2_row32_col3\" class=\"data row32 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row32_col4\" class=\"data row32 col4\" >82.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row33_col0\" class=\"data row33 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row33_col1\" class=\"data row33 col1\" >24/09/10</td>\n",
       "      <td id=\"T_aa7d2_row33_col2\" class=\"data row33 col2\" >$ 1351791.03</td>\n",
       "      <td id=\"T_aa7d2_row33_col3\" class=\"data row33 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row33_col4\" class=\"data row33 col4\" >80.94º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row34_col0\" class=\"data row34 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row34_col1\" class=\"data row34 col1\" >10/01/10</td>\n",
       "      <td id=\"T_aa7d2_row34_col2\" class=\"data row34 col2\" >$ 1453329.50</td>\n",
       "      <td id=\"T_aa7d2_row34_col3\" class=\"data row34 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row34_col4\" class=\"data row34 col4\" >71.89º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row35_col0\" class=\"data row35 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row35_col1\" class=\"data row35 col1\" >10/08/10</td>\n",
       "      <td id=\"T_aa7d2_row35_col2\" class=\"data row35 col2\" >$ 1508239.93</td>\n",
       "      <td id=\"T_aa7d2_row35_col3\" class=\"data row35 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row35_col4\" class=\"data row35 col4\" >63.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row36_col0\" class=\"data row36 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row36_col1\" class=\"data row36 col1\" >15/10/10</td>\n",
       "      <td id=\"T_aa7d2_row36_col2\" class=\"data row36 col2\" >$ 1459409.10</td>\n",
       "      <td id=\"T_aa7d2_row36_col3\" class=\"data row36 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row36_col4\" class=\"data row36 col4\" >67.18º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row37_col0\" class=\"data row37 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row37_col1\" class=\"data row37 col1\" >22/10/10</td>\n",
       "      <td id=\"T_aa7d2_row37_col2\" class=\"data row37 col2\" >$ 1345454.00</td>\n",
       "      <td id=\"T_aa7d2_row37_col3\" class=\"data row37 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row37_col4\" class=\"data row37 col4\" >69.86º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row38_col0\" class=\"data row38 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row38_col1\" class=\"data row38 col1\" >29/10/10</td>\n",
       "      <td id=\"T_aa7d2_row38_col2\" class=\"data row38 col2\" >$ 1384209.22</td>\n",
       "      <td id=\"T_aa7d2_row38_col3\" class=\"data row38 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row38_col4\" class=\"data row38 col4\" >69.64º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row39_col0\" class=\"data row39 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row39_col1\" class=\"data row39 col1\" >11/05/10</td>\n",
       "      <td id=\"T_aa7d2_row39_col2\" class=\"data row39 col2\" >$ 1551659.28</td>\n",
       "      <td id=\"T_aa7d2_row39_col3\" class=\"data row39 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row39_col4\" class=\"data row39 col4\" >58.74º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row40_col0\" class=\"data row40 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row40_col1\" class=\"data row40 col1\" >11/12/10</td>\n",
       "      <td id=\"T_aa7d2_row40_col2\" class=\"data row40 col2\" >$ 1494479.49</td>\n",
       "      <td id=\"T_aa7d2_row40_col3\" class=\"data row40 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row40_col4\" class=\"data row40 col4\" >59.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row41_col0\" class=\"data row41 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row41_col1\" class=\"data row41 col1\" >19/11/10</td>\n",
       "      <td id=\"T_aa7d2_row41_col2\" class=\"data row41 col2\" >$ 1483784.18</td>\n",
       "      <td id=\"T_aa7d2_row41_col3\" class=\"data row41 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row41_col4\" class=\"data row41 col4\" >51.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row42_col0\" class=\"data row42 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row42_col1\" class=\"data row42 col1\" >26/11/10</td>\n",
       "      <td id=\"T_aa7d2_row42_col2\" class=\"data row42 col2\" >$ 1955624.11</td>\n",
       "      <td id=\"T_aa7d2_row42_col3\" class=\"data row42 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row42_col4\" class=\"data row42 col4\" >64.52º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row43_col0\" class=\"data row43 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row43_col1\" class=\"data row43 col1\" >12/03/10</td>\n",
       "      <td id=\"T_aa7d2_row43_col2\" class=\"data row43 col2\" >$ 1548033.78</td>\n",
       "      <td id=\"T_aa7d2_row43_col3\" class=\"data row43 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row43_col4\" class=\"data row43 col4\" >49.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row44_col0\" class=\"data row44 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row44_col1\" class=\"data row44 col1\" >12/10/10</td>\n",
       "      <td id=\"T_aa7d2_row44_col2\" class=\"data row44 col2\" >$ 1682614.26</td>\n",
       "      <td id=\"T_aa7d2_row44_col3\" class=\"data row44 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row44_col4\" class=\"data row44 col4\" >46.33º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row45_col0\" class=\"data row45 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row45_col1\" class=\"data row45 col1\" >17/12/10</td>\n",
       "      <td id=\"T_aa7d2_row45_col2\" class=\"data row45 col2\" >$ 1891034.93</td>\n",
       "      <td id=\"T_aa7d2_row45_col3\" class=\"data row45 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row45_col4\" class=\"data row45 col4\" >49.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row46_col0\" class=\"data row46 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row46_col1\" class=\"data row46 col1\" >24/12/10</td>\n",
       "      <td id=\"T_aa7d2_row46_col2\" class=\"data row46 col2\" >$ 2387950.20</td>\n",
       "      <td id=\"T_aa7d2_row46_col3\" class=\"data row46 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row46_col4\" class=\"data row46 col4\" >52.33º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row47_col0\" class=\"data row47 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row47_col1\" class=\"data row47 col1\" >31/12/10</td>\n",
       "      <td id=\"T_aa7d2_row47_col2\" class=\"data row47 col2\" >$ 1367320.01</td>\n",
       "      <td id=\"T_aa7d2_row47_col3\" class=\"data row47 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row47_col4\" class=\"data row47 col4\" >48.43º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row48_col0\" class=\"data row48 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row48_col1\" class=\"data row48 col1\" >01/07/11</td>\n",
       "      <td id=\"T_aa7d2_row48_col2\" class=\"data row48 col2\" >$ 1444732.28</td>\n",
       "      <td id=\"T_aa7d2_row48_col3\" class=\"data row48 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row48_col4\" class=\"data row48 col4\" >48.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row49_col0\" class=\"data row49 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row49_col1\" class=\"data row49 col1\" >14/01/11</td>\n",
       "      <td id=\"T_aa7d2_row49_col2\" class=\"data row49 col2\" >$ 1391013.96</td>\n",
       "      <td id=\"T_aa7d2_row49_col3\" class=\"data row49 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row49_col4\" class=\"data row49 col4\" >35.40º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row50_col0\" class=\"data row50 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row50_col1\" class=\"data row50 col1\" >21/01/11</td>\n",
       "      <td id=\"T_aa7d2_row50_col2\" class=\"data row50 col2\" >$ 1327405.42</td>\n",
       "      <td id=\"T_aa7d2_row50_col3\" class=\"data row50 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row50_col4\" class=\"data row50 col4\" >44.04º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row51_col0\" class=\"data row51 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row51_col1\" class=\"data row51 col1\" >28/01/11</td>\n",
       "      <td id=\"T_aa7d2_row51_col2\" class=\"data row51 col2\" >$ 1316899.31</td>\n",
       "      <td id=\"T_aa7d2_row51_col3\" class=\"data row51 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row51_col4\" class=\"data row51 col4\" >43.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row52_col0\" class=\"data row52 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row52_col1\" class=\"data row52 col1\" >02/04/11</td>\n",
       "      <td id=\"T_aa7d2_row52_col2\" class=\"data row52 col2\" >$ 1606629.58</td>\n",
       "      <td id=\"T_aa7d2_row52_col3\" class=\"data row52 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row52_col4\" class=\"data row52 col4\" >42.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row53_col0\" class=\"data row53 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row53_col1\" class=\"data row53 col1\" >02/11/11</td>\n",
       "      <td id=\"T_aa7d2_row53_col2\" class=\"data row53 col2\" >$ 1649614.93</td>\n",
       "      <td id=\"T_aa7d2_row53_col3\" class=\"data row53 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row53_col4\" class=\"data row53 col4\" >36.39º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row54_col0\" class=\"data row54 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row54_col1\" class=\"data row54 col1\" >18/02/11</td>\n",
       "      <td id=\"T_aa7d2_row54_col2\" class=\"data row54 col2\" >$ 1686842.78</td>\n",
       "      <td id=\"T_aa7d2_row54_col3\" class=\"data row54 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row54_col4\" class=\"data row54 col4\" >57.36º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row55_col0\" class=\"data row55 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row55_col1\" class=\"data row55 col1\" >25/02/11</td>\n",
       "      <td id=\"T_aa7d2_row55_col2\" class=\"data row55 col2\" >$ 1456800.28</td>\n",
       "      <td id=\"T_aa7d2_row55_col3\" class=\"data row55 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row55_col4\" class=\"data row55 col4\" >62.90º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row56_col0\" class=\"data row56 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row56_col1\" class=\"data row56 col1\" >03/04/11</td>\n",
       "      <td id=\"T_aa7d2_row56_col2\" class=\"data row56 col2\" >$ 1636263.41</td>\n",
       "      <td id=\"T_aa7d2_row56_col3\" class=\"data row56 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row56_col4\" class=\"data row56 col4\" >59.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row57_col0\" class=\"data row57 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row57_col1\" class=\"data row57 col1\" >03/11/11</td>\n",
       "      <td id=\"T_aa7d2_row57_col2\" class=\"data row57 col2\" >$ 1553191.63</td>\n",
       "      <td id=\"T_aa7d2_row57_col3\" class=\"data row57 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row57_col4\" class=\"data row57 col4\" >53.56º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row58_col0\" class=\"data row58 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row58_col1\" class=\"data row58 col1\" >18/03/11</td>\n",
       "      <td id=\"T_aa7d2_row58_col2\" class=\"data row58 col2\" >$ 1576818.06</td>\n",
       "      <td id=\"T_aa7d2_row58_col3\" class=\"data row58 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row58_col4\" class=\"data row58 col4\" >62.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row59_col0\" class=\"data row59 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row59_col1\" class=\"data row59 col1\" >25/03/11</td>\n",
       "      <td id=\"T_aa7d2_row59_col2\" class=\"data row59 col2\" >$ 1541102.38</td>\n",
       "      <td id=\"T_aa7d2_row59_col3\" class=\"data row59 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row59_col4\" class=\"data row59 col4\" >69.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row60_col0\" class=\"data row60 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row60_col1\" class=\"data row60 col1\" >04/01/11</td>\n",
       "      <td id=\"T_aa7d2_row60_col2\" class=\"data row60 col2\" >$ 1495064.75</td>\n",
       "      <td id=\"T_aa7d2_row60_col3\" class=\"data row60 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row60_col4\" class=\"data row60 col4\" >59.17º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row61_col0\" class=\"data row61 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row61_col1\" class=\"data row61 col1\" >04/08/11</td>\n",
       "      <td id=\"T_aa7d2_row61_col2\" class=\"data row61 col2\" >$ 1614259.35</td>\n",
       "      <td id=\"T_aa7d2_row61_col3\" class=\"data row61 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row61_col4\" class=\"data row61 col4\" >67.84º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row62_col0\" class=\"data row62 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row62_col1\" class=\"data row62 col1\" >15/04/11</td>\n",
       "      <td id=\"T_aa7d2_row62_col2\" class=\"data row62 col2\" >$ 1559889.00</td>\n",
       "      <td id=\"T_aa7d2_row62_col3\" class=\"data row62 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row62_col4\" class=\"data row62 col4\" >71.27º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row63_col0\" class=\"data row63 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row63_col1\" class=\"data row63 col1\" >22/04/11</td>\n",
       "      <td id=\"T_aa7d2_row63_col2\" class=\"data row63 col2\" >$ 1564819.81</td>\n",
       "      <td id=\"T_aa7d2_row63_col3\" class=\"data row63 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row63_col4\" class=\"data row63 col4\" >72.99º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row64_col0\" class=\"data row64 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row64_col1\" class=\"data row64 col1\" >29/04/11</td>\n",
       "      <td id=\"T_aa7d2_row64_col2\" class=\"data row64 col2\" >$ 1455090.69</td>\n",
       "      <td id=\"T_aa7d2_row64_col3\" class=\"data row64 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row64_col4\" class=\"data row64 col4\" >72.03º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row65_col0\" class=\"data row65 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row65_col1\" class=\"data row65 col1\" >05/06/11</td>\n",
       "      <td id=\"T_aa7d2_row65_col2\" class=\"data row65 col2\" >$ 1629391.28</td>\n",
       "      <td id=\"T_aa7d2_row65_col3\" class=\"data row65 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row65_col4\" class=\"data row65 col4\" >64.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row66_col0\" class=\"data row66 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row66_col1\" class=\"data row66 col1\" >13/05/11</td>\n",
       "      <td id=\"T_aa7d2_row66_col2\" class=\"data row66 col2\" >$ 1604775.58</td>\n",
       "      <td id=\"T_aa7d2_row66_col3\" class=\"data row66 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row66_col4\" class=\"data row66 col4\" >75.64º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row67_col0\" class=\"data row67 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row67_col1\" class=\"data row67 col1\" >20/05/11</td>\n",
       "      <td id=\"T_aa7d2_row67_col2\" class=\"data row67 col2\" >$ 1428218.27</td>\n",
       "      <td id=\"T_aa7d2_row67_col3\" class=\"data row67 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row67_col4\" class=\"data row67 col4\" >67.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row68_col0\" class=\"data row68 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row68_col1\" class=\"data row68 col1\" >27/05/11</td>\n",
       "      <td id=\"T_aa7d2_row68_col2\" class=\"data row68 col2\" >$ 1466046.67</td>\n",
       "      <td id=\"T_aa7d2_row68_col3\" class=\"data row68 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row68_col4\" class=\"data row68 col4\" >77.72º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row69_col0\" class=\"data row69 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row69_col1\" class=\"data row69 col1\" >06/03/11</td>\n",
       "      <td id=\"T_aa7d2_row69_col2\" class=\"data row69 col2\" >$ 1635078.41</td>\n",
       "      <td id=\"T_aa7d2_row69_col3\" class=\"data row69 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row69_col4\" class=\"data row69 col4\" >83.00º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row70_col0\" class=\"data row70 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row70_col1\" class=\"data row70 col1\" >06/10/11</td>\n",
       "      <td id=\"T_aa7d2_row70_col2\" class=\"data row70 col2\" >$ 1588948.32</td>\n",
       "      <td id=\"T_aa7d2_row70_col3\" class=\"data row70 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row70_col4\" class=\"data row70 col4\" >83.13º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row71_col0\" class=\"data row71 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row71_col1\" class=\"data row71 col1\" >17/06/11</td>\n",
       "      <td id=\"T_aa7d2_row71_col2\" class=\"data row71 col2\" >$ 1532114.86</td>\n",
       "      <td id=\"T_aa7d2_row71_col3\" class=\"data row71 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row71_col4\" class=\"data row71 col4\" >86.41º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row72_col0\" class=\"data row72 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row72_col1\" class=\"data row72 col1\" >24/06/11</td>\n",
       "      <td id=\"T_aa7d2_row72_col2\" class=\"data row72 col2\" >$ 1438830.15</td>\n",
       "      <td id=\"T_aa7d2_row72_col3\" class=\"data row72 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row72_col4\" class=\"data row72 col4\" >83.58º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row73_col0\" class=\"data row73 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row73_col1\" class=\"data row73 col1\" >07/01/11</td>\n",
       "      <td id=\"T_aa7d2_row73_col2\" class=\"data row73 col2\" >$ 1488538.09</td>\n",
       "      <td id=\"T_aa7d2_row73_col3\" class=\"data row73 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row73_col4\" class=\"data row73 col4\" >85.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row74_col0\" class=\"data row74 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row74_col1\" class=\"data row74 col1\" >07/08/11</td>\n",
       "      <td id=\"T_aa7d2_row74_col2\" class=\"data row74 col2\" >$ 1534849.64</td>\n",
       "      <td id=\"T_aa7d2_row74_col3\" class=\"data row74 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row74_col4\" class=\"data row74 col4\" >85.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row75_col0\" class=\"data row75 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row75_col1\" class=\"data row75 col1\" >15/07/11</td>\n",
       "      <td id=\"T_aa7d2_row75_col2\" class=\"data row75 col2\" >$ 1455119.97</td>\n",
       "      <td id=\"T_aa7d2_row75_col3\" class=\"data row75 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row75_col4\" class=\"data row75 col4\" >88.54º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row76_col0\" class=\"data row76 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row76_col1\" class=\"data row76 col1\" >22/07/11</td>\n",
       "      <td id=\"T_aa7d2_row76_col2\" class=\"data row76 col2\" >$ 1396926.82</td>\n",
       "      <td id=\"T_aa7d2_row76_col3\" class=\"data row76 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row76_col4\" class=\"data row76 col4\" >85.77º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row77_col0\" class=\"data row77 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row77_col1\" class=\"data row77 col1\" >29/07/11</td>\n",
       "      <td id=\"T_aa7d2_row77_col2\" class=\"data row77 col2\" >$ 1352219.79</td>\n",
       "      <td id=\"T_aa7d2_row77_col3\" class=\"data row77 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row77_col4\" class=\"data row77 col4\" >86.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row78_col0\" class=\"data row78 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row78_col1\" class=\"data row78 col1\" >08/05/11</td>\n",
       "      <td id=\"T_aa7d2_row78_col2\" class=\"data row78 col2\" >$ 1624383.75</td>\n",
       "      <td id=\"T_aa7d2_row78_col3\" class=\"data row78 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row78_col4\" class=\"data row78 col4\" >91.65º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row79_col0\" class=\"data row79 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row79_col1\" class=\"data row79 col1\" >08/12/11</td>\n",
       "      <td id=\"T_aa7d2_row79_col2\" class=\"data row79 col2\" >$ 1525147.09</td>\n",
       "      <td id=\"T_aa7d2_row79_col3\" class=\"data row79 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row79_col4\" class=\"data row79 col4\" >90.76º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row80_col0\" class=\"data row80 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row80_col1\" class=\"data row80 col1\" >19/08/11</td>\n",
       "      <td id=\"T_aa7d2_row80_col2\" class=\"data row80 col2\" >$ 1530761.43</td>\n",
       "      <td id=\"T_aa7d2_row80_col3\" class=\"data row80 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row80_col4\" class=\"data row80 col4\" >89.94º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row81_col0\" class=\"data row81 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row81_col1\" class=\"data row81 col1\" >26/08/11</td>\n",
       "      <td id=\"T_aa7d2_row81_col2\" class=\"data row81 col2\" >$ 1464693.46</td>\n",
       "      <td id=\"T_aa7d2_row81_col3\" class=\"data row81 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row81_col4\" class=\"data row81 col4\" >87.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row82_col0\" class=\"data row82 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row82_col1\" class=\"data row82 col1\" >09/02/11</td>\n",
       "      <td id=\"T_aa7d2_row82_col2\" class=\"data row82 col2\" >$ 1550229.22</td>\n",
       "      <td id=\"T_aa7d2_row82_col3\" class=\"data row82 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row82_col4\" class=\"data row82 col4\" >87.83º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row83_col0\" class=\"data row83 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row83_col1\" class=\"data row83 col1\" >09/09/11</td>\n",
       "      <td id=\"T_aa7d2_row83_col2\" class=\"data row83 col2\" >$ 1540471.24</td>\n",
       "      <td id=\"T_aa7d2_row83_col3\" class=\"data row83 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row83_col4\" class=\"data row83 col4\" >76.00º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row84_col0\" class=\"data row84 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row84_col1\" class=\"data row84 col1\" >16/09/11</td>\n",
       "      <td id=\"T_aa7d2_row84_col2\" class=\"data row84 col2\" >$ 1514259.78</td>\n",
       "      <td id=\"T_aa7d2_row84_col3\" class=\"data row84 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row84_col4\" class=\"data row84 col4\" >79.94º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row85_col0\" class=\"data row85 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row85_col1\" class=\"data row85 col1\" >23/09/11</td>\n",
       "      <td id=\"T_aa7d2_row85_col2\" class=\"data row85 col2\" >$ 1380020.27</td>\n",
       "      <td id=\"T_aa7d2_row85_col3\" class=\"data row85 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row85_col4\" class=\"data row85 col4\" >75.80º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row86_col0\" class=\"data row86 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row86_col1\" class=\"data row86 col1\" >30/09/11</td>\n",
       "      <td id=\"T_aa7d2_row86_col2\" class=\"data row86 col2\" >$ 1394561.83</td>\n",
       "      <td id=\"T_aa7d2_row86_col3\" class=\"data row86 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row86_col4\" class=\"data row86 col4\" >79.69º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row87_col0\" class=\"data row87 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row87_col1\" class=\"data row87 col1\" >10/07/11</td>\n",
       "      <td id=\"T_aa7d2_row87_col2\" class=\"data row87 col2\" >$ 1630989.95</td>\n",
       "      <td id=\"T_aa7d2_row87_col3\" class=\"data row87 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row87_col4\" class=\"data row87 col4\" >69.31º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row88_col0\" class=\"data row88 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row88_col1\" class=\"data row88 col1\" >14/10/11</td>\n",
       "      <td id=\"T_aa7d2_row88_col2\" class=\"data row88 col2\" >$ 1493525.93</td>\n",
       "      <td id=\"T_aa7d2_row88_col3\" class=\"data row88 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row88_col4\" class=\"data row88 col4\" >71.74º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row89_col0\" class=\"data row89 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row89_col1\" class=\"data row89 col1\" >21/10/11</td>\n",
       "      <td id=\"T_aa7d2_row89_col2\" class=\"data row89 col2\" >$ 1502562.78</td>\n",
       "      <td id=\"T_aa7d2_row89_col3\" class=\"data row89 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row89_col4\" class=\"data row89 col4\" >63.71º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row90_col0\" class=\"data row90 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row90_col1\" class=\"data row90 col1\" >28/10/11</td>\n",
       "      <td id=\"T_aa7d2_row90_col2\" class=\"data row90 col2\" >$ 1445249.09</td>\n",
       "      <td id=\"T_aa7d2_row90_col3\" class=\"data row90 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row90_col4\" class=\"data row90 col4\" >66.57º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row91_col0\" class=\"data row91 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row91_col1\" class=\"data row91 col1\" >11/04/11</td>\n",
       "      <td id=\"T_aa7d2_row91_col2\" class=\"data row91 col2\" >$ 1697229.58</td>\n",
       "      <td id=\"T_aa7d2_row91_col3\" class=\"data row91 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row91_col4\" class=\"data row91 col4\" >54.98º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row92_col0\" class=\"data row92 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row92_col1\" class=\"data row92 col1\" >11/11/11</td>\n",
       "      <td id=\"T_aa7d2_row92_col2\" class=\"data row92 col2\" >$ 1594938.89</td>\n",
       "      <td id=\"T_aa7d2_row92_col3\" class=\"data row92 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row92_col4\" class=\"data row92 col4\" >59.11º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row93_col0\" class=\"data row93 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row93_col1\" class=\"data row93 col1\" >18/11/11</td>\n",
       "      <td id=\"T_aa7d2_row93_col2\" class=\"data row93 col2\" >$ 1539483.70</td>\n",
       "      <td id=\"T_aa7d2_row93_col3\" class=\"data row93 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row93_col4\" class=\"data row93 col4\" >62.25º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row94_col0\" class=\"data row94 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row94_col1\" class=\"data row94 col1\" >25/11/11</td>\n",
       "      <td id=\"T_aa7d2_row94_col2\" class=\"data row94 col2\" >$ 2033320.66</td>\n",
       "      <td id=\"T_aa7d2_row94_col3\" class=\"data row94 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row94_col4\" class=\"data row94 col4\" >60.14º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row95_col0\" class=\"data row95 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row95_col1\" class=\"data row95 col1\" >12/02/11</td>\n",
       "      <td id=\"T_aa7d2_row95_col2\" class=\"data row95 col2\" >$ 1584083.95</td>\n",
       "      <td id=\"T_aa7d2_row95_col3\" class=\"data row95 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row95_col4\" class=\"data row95 col4\" >48.91º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row96_col0\" class=\"data row96 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row96_col1\" class=\"data row96 col1\" >12/09/11</td>\n",
       "      <td id=\"T_aa7d2_row96_col2\" class=\"data row96 col2\" >$ 1799682.38</td>\n",
       "      <td id=\"T_aa7d2_row96_col3\" class=\"data row96 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row96_col4\" class=\"data row96 col4\" >43.93º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row97_col0\" class=\"data row97 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row97_col1\" class=\"data row97 col1\" >16/12/11</td>\n",
       "      <td id=\"T_aa7d2_row97_col2\" class=\"data row97 col2\" >$ 1881176.67</td>\n",
       "      <td id=\"T_aa7d2_row97_col3\" class=\"data row97 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row97_col4\" class=\"data row97 col4\" >51.63º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row98_col0\" class=\"data row98 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row98_col1\" class=\"data row98 col1\" >23/12/11</td>\n",
       "      <td id=\"T_aa7d2_row98_col2\" class=\"data row98 col2\" >$ 2270188.99</td>\n",
       "      <td id=\"T_aa7d2_row98_col3\" class=\"data row98 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row98_col4\" class=\"data row98 col4\" >47.96º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row99_col0\" class=\"data row99 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row99_col1\" class=\"data row99 col1\" >30/12/11</td>\n",
       "      <td id=\"T_aa7d2_row99_col2\" class=\"data row99 col2\" >$ 1497462.72</td>\n",
       "      <td id=\"T_aa7d2_row99_col3\" class=\"data row99 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row99_col4\" class=\"data row99 col4\" >44.55º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row100_col0\" class=\"data row100 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row100_col1\" class=\"data row100 col1\" >01/06/12</td>\n",
       "      <td id=\"T_aa7d2_row100_col2\" class=\"data row100 col2\" >$ 1550369.92</td>\n",
       "      <td id=\"T_aa7d2_row100_col3\" class=\"data row100 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row100_col4\" class=\"data row100 col4\" >49.01º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row101_col0\" class=\"data row101 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row101_col1\" class=\"data row101 col1\" >13/01/12</td>\n",
       "      <td id=\"T_aa7d2_row101_col2\" class=\"data row101 col2\" >$ 1459601.17</td>\n",
       "      <td id=\"T_aa7d2_row101_col3\" class=\"data row101 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row101_col4\" class=\"data row101 col4\" >48.53º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row102_col0\" class=\"data row102 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row102_col1\" class=\"data row102 col1\" >20/01/12</td>\n",
       "      <td id=\"T_aa7d2_row102_col2\" class=\"data row102 col2\" >$ 1394393.84</td>\n",
       "      <td id=\"T_aa7d2_row102_col3\" class=\"data row102 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row102_col4\" class=\"data row102 col4\" >54.11º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row103_col0\" class=\"data row103 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row103_col1\" class=\"data row103 col1\" >27/01/12</td>\n",
       "      <td id=\"T_aa7d2_row103_col2\" class=\"data row103 col2\" >$ 1319325.59</td>\n",
       "      <td id=\"T_aa7d2_row103_col3\" class=\"data row103 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row103_col4\" class=\"data row103 col4\" >54.26º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row104_col0\" class=\"data row104 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row104_col1\" class=\"data row104 col1\" >02/03/12</td>\n",
       "      <td id=\"T_aa7d2_row104_col2\" class=\"data row104 col2\" >$ 1636339.65</td>\n",
       "      <td id=\"T_aa7d2_row104_col3\" class=\"data row104 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row104_col4\" class=\"data row104 col4\" >56.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row105_col0\" class=\"data row105 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row105_col1\" class=\"data row105 col1\" >02/10/12</td>\n",
       "      <td id=\"T_aa7d2_row105_col2\" class=\"data row105 col2\" >$ 1802477.43</td>\n",
       "      <td id=\"T_aa7d2_row105_col3\" class=\"data row105 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row105_col4\" class=\"data row105 col4\" >48.02º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row106_col0\" class=\"data row106 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row106_col1\" class=\"data row106 col1\" >17/02/12</td>\n",
       "      <td id=\"T_aa7d2_row106_col2\" class=\"data row106 col2\" >$ 1819870.00</td>\n",
       "      <td id=\"T_aa7d2_row106_col3\" class=\"data row106 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row106_col4\" class=\"data row106 col4\" >45.32º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row107_col0\" class=\"data row107 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row107_col1\" class=\"data row107 col1\" >24/02/12</td>\n",
       "      <td id=\"T_aa7d2_row107_col2\" class=\"data row107 col2\" >$ 1539387.83</td>\n",
       "      <td id=\"T_aa7d2_row107_col3\" class=\"data row107 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row107_col4\" class=\"data row107 col4\" >57.25º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row108_col0\" class=\"data row108 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row108_col1\" class=\"data row108 col1\" >03/02/12</td>\n",
       "      <td id=\"T_aa7d2_row108_col2\" class=\"data row108 col2\" >$ 1688420.76</td>\n",
       "      <td id=\"T_aa7d2_row108_col3\" class=\"data row108 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row108_col4\" class=\"data row108 col4\" >60.96º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row109_col0\" class=\"data row109 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row109_col1\" class=\"data row109 col1\" >03/09/12</td>\n",
       "      <td id=\"T_aa7d2_row109_col2\" class=\"data row109 col2\" >$ 1675431.16</td>\n",
       "      <td id=\"T_aa7d2_row109_col3\" class=\"data row109 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row109_col4\" class=\"data row109 col4\" >58.76º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row110_col0\" class=\"data row110 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row110_col1\" class=\"data row110 col1\" >16/03/12</td>\n",
       "      <td id=\"T_aa7d2_row110_col2\" class=\"data row110 col2\" >$ 1677472.78</td>\n",
       "      <td id=\"T_aa7d2_row110_col3\" class=\"data row110 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row110_col4\" class=\"data row110 col4\" >64.74º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row111_col0\" class=\"data row111 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row111_col1\" class=\"data row111 col1\" >23/03/12</td>\n",
       "      <td id=\"T_aa7d2_row111_col2\" class=\"data row111 col2\" >$ 1511068.07</td>\n",
       "      <td id=\"T_aa7d2_row111_col3\" class=\"data row111 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row111_col4\" class=\"data row111 col4\" >65.93º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row112_col0\" class=\"data row112 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row112_col1\" class=\"data row112 col1\" >30/03/12</td>\n",
       "      <td id=\"T_aa7d2_row112_col2\" class=\"data row112 col2\" >$ 1649604.63</td>\n",
       "      <td id=\"T_aa7d2_row112_col3\" class=\"data row112 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row112_col4\" class=\"data row112 col4\" >67.61º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row113_col0\" class=\"data row113 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row113_col1\" class=\"data row113 col1\" >04/06/12</td>\n",
       "      <td id=\"T_aa7d2_row113_col2\" class=\"data row113 col2\" >$ 1899676.88</td>\n",
       "      <td id=\"T_aa7d2_row113_col3\" class=\"data row113 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row113_col4\" class=\"data row113 col4\" >70.43º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row114_col0\" class=\"data row114 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row114_col1\" class=\"data row114 col1\" >13/04/12</td>\n",
       "      <td id=\"T_aa7d2_row114_col2\" class=\"data row114 col2\" >$ 1621031.70</td>\n",
       "      <td id=\"T_aa7d2_row114_col3\" class=\"data row114 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row114_col4\" class=\"data row114 col4\" >69.07º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row115_col0\" class=\"data row115 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row115_col1\" class=\"data row115 col1\" >20/04/12</td>\n",
       "      <td id=\"T_aa7d2_row115_col2\" class=\"data row115 col2\" >$ 1521577.87</td>\n",
       "      <td id=\"T_aa7d2_row115_col3\" class=\"data row115 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row115_col4\" class=\"data row115 col4\" >66.76º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row116_col0\" class=\"data row116 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row116_col1\" class=\"data row116 col1\" >27/04/12</td>\n",
       "      <td id=\"T_aa7d2_row116_col2\" class=\"data row116 col2\" >$ 1468928.37</td>\n",
       "      <td id=\"T_aa7d2_row116_col3\" class=\"data row116 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row116_col4\" class=\"data row116 col4\" >67.23º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row117_col0\" class=\"data row117 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row117_col1\" class=\"data row117 col1\" >05/04/12</td>\n",
       "      <td id=\"T_aa7d2_row117_col2\" class=\"data row117 col2\" >$ 1684519.99</td>\n",
       "      <td id=\"T_aa7d2_row117_col3\" class=\"data row117 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row117_col4\" class=\"data row117 col4\" >75.55º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row118_col0\" class=\"data row118 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row118_col1\" class=\"data row118 col1\" >05/11/12</td>\n",
       "      <td id=\"T_aa7d2_row118_col2\" class=\"data row118 col2\" >$ 1611096.05</td>\n",
       "      <td id=\"T_aa7d2_row118_col3\" class=\"data row118 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row118_col4\" class=\"data row118 col4\" >73.77º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row119_col0\" class=\"data row119 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row119_col1\" class=\"data row119 col1\" >18/05/12</td>\n",
       "      <td id=\"T_aa7d2_row119_col2\" class=\"data row119 col2\" >$ 1595901.87</td>\n",
       "      <td id=\"T_aa7d2_row119_col3\" class=\"data row119 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row119_col4\" class=\"data row119 col4\" >70.33º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row120_col0\" class=\"data row120 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row120_col1\" class=\"data row120 col1\" >25/05/12</td>\n",
       "      <td id=\"T_aa7d2_row120_col2\" class=\"data row120 col2\" >$ 1555444.55</td>\n",
       "      <td id=\"T_aa7d2_row120_col3\" class=\"data row120 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row120_col4\" class=\"data row120 col4\" >77.22º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row121_col0\" class=\"data row121 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row121_col1\" class=\"data row121 col1\" >06/01/12</td>\n",
       "      <td id=\"T_aa7d2_row121_col2\" class=\"data row121 col2\" >$ 1624477.58</td>\n",
       "      <td id=\"T_aa7d2_row121_col3\" class=\"data row121 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row121_col4\" class=\"data row121 col4\" >77.95º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row122_col0\" class=\"data row122 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row122_col1\" class=\"data row122 col1\" >06/08/12</td>\n",
       "      <td id=\"T_aa7d2_row122_col2\" class=\"data row122 col2\" >$ 1697230.96</td>\n",
       "      <td id=\"T_aa7d2_row122_col3\" class=\"data row122 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row122_col4\" class=\"data row122 col4\" >78.30º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row123_col0\" class=\"data row123 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row123_col1\" class=\"data row123 col1\" >15/06/12</td>\n",
       "      <td id=\"T_aa7d2_row123_col2\" class=\"data row123 col2\" >$ 1630607.00</td>\n",
       "      <td id=\"T_aa7d2_row123_col3\" class=\"data row123 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row123_col4\" class=\"data row123 col4\" >79.35º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row124_col0\" class=\"data row124 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row124_col1\" class=\"data row124 col1\" >22/06/12</td>\n",
       "      <td id=\"T_aa7d2_row124_col2\" class=\"data row124 col2\" >$ 1527845.81</td>\n",
       "      <td id=\"T_aa7d2_row124_col3\" class=\"data row124 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row124_col4\" class=\"data row124 col4\" >78.39º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row125_col0\" class=\"data row125 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row125_col1\" class=\"data row125 col1\" >29/06/12</td>\n",
       "      <td id=\"T_aa7d2_row125_col2\" class=\"data row125 col2\" >$ 1540421.49</td>\n",
       "      <td id=\"T_aa7d2_row125_col3\" class=\"data row125 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row125_col4\" class=\"data row125 col4\" >84.88º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row126_col0\" class=\"data row126 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row126_col1\" class=\"data row126 col1\" >07/06/12</td>\n",
       "      <td id=\"T_aa7d2_row126_col2\" class=\"data row126 col2\" >$ 1769854.16</td>\n",
       "      <td id=\"T_aa7d2_row126_col3\" class=\"data row126 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row126_col4\" class=\"data row126 col4\" >81.57º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row127_col0\" class=\"data row127 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row127_col1\" class=\"data row127 col1\" >13/07/12</td>\n",
       "      <td id=\"T_aa7d2_row127_col2\" class=\"data row127 col2\" >$ 1527014.04</td>\n",
       "      <td id=\"T_aa7d2_row127_col3\" class=\"data row127 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row127_col4\" class=\"data row127 col4\" >77.12º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row128_col0\" class=\"data row128 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row128_col1\" class=\"data row128 col1\" >20/07/12</td>\n",
       "      <td id=\"T_aa7d2_row128_col2\" class=\"data row128 col2\" >$ 1497954.76</td>\n",
       "      <td id=\"T_aa7d2_row128_col3\" class=\"data row128 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row128_col4\" class=\"data row128 col4\" >80.42º</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_aa7d2_row129_col0\" class=\"data row129 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row129_col1\" class=\"data row129 col1\" >27/07/12</td>\n",
       "      <td id=\"T_aa7d2_row129_col2\" class=\"data row129 col2\" >$ 1439123.71</td>\n",
       "      <td id=\"T_aa7d2_row129_col3\" class=\"data row129 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row129_col4\" class=\"data row129 col4\" >82.66º</td>\n",
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       "      <td id=\"T_aa7d2_row130_col0\" class=\"data row130 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row130_col1\" class=\"data row130 col1\" >08/03/12</td>\n",
       "      <td id=\"T_aa7d2_row130_col2\" class=\"data row130 col2\" >$ 1631135.79</td>\n",
       "      <td id=\"T_aa7d2_row130_col3\" class=\"data row130 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row130_col4\" class=\"data row130 col4\" >86.11º</td>\n",
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       "      <td id=\"T_aa7d2_row131_col0\" class=\"data row131 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row131_col1\" class=\"data row131 col1\" >08/10/12</td>\n",
       "      <td id=\"T_aa7d2_row131_col2\" class=\"data row131 col2\" >$ 1592409.97</td>\n",
       "      <td id=\"T_aa7d2_row131_col3\" class=\"data row131 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row131_col4\" class=\"data row131 col4\" >85.05º</td>\n",
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       "      <td id=\"T_aa7d2_row132_col0\" class=\"data row132 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row132_col1\" class=\"data row132 col1\" >17/08/12</td>\n",
       "      <td id=\"T_aa7d2_row132_col2\" class=\"data row132 col2\" >$ 1597868.05</td>\n",
       "      <td id=\"T_aa7d2_row132_col3\" class=\"data row132 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row132_col4\" class=\"data row132 col4\" >84.85º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row133_col0\" class=\"data row133 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row133_col1\" class=\"data row133 col1\" >24/08/12</td>\n",
       "      <td id=\"T_aa7d2_row133_col2\" class=\"data row133 col2\" >$ 1494122.38</td>\n",
       "      <td id=\"T_aa7d2_row133_col3\" class=\"data row133 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row133_col4\" class=\"data row133 col4\" >77.66º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row134_col0\" class=\"data row134 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row134_col1\" class=\"data row134 col1\" >31/08/12</td>\n",
       "      <td id=\"T_aa7d2_row134_col2\" class=\"data row134 col2\" >$ 1582083.40</td>\n",
       "      <td id=\"T_aa7d2_row134_col3\" class=\"data row134 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row134_col4\" class=\"data row134 col4\" >80.49º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row135_col0\" class=\"data row135 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row135_col1\" class=\"data row135 col1\" >09/07/12</td>\n",
       "      <td id=\"T_aa7d2_row135_col2\" class=\"data row135 col2\" >$ 1661767.33</td>\n",
       "      <td id=\"T_aa7d2_row135_col3\" class=\"data row135 col3\" >True</td>\n",
       "      <td id=\"T_aa7d2_row135_col4\" class=\"data row135 col4\" >83.96º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row136_col0\" class=\"data row136 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row136_col1\" class=\"data row136 col1\" >14/09/12</td>\n",
       "      <td id=\"T_aa7d2_row136_col2\" class=\"data row136 col2\" >$ 1517428.87</td>\n",
       "      <td id=\"T_aa7d2_row136_col3\" class=\"data row136 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row136_col4\" class=\"data row136 col4\" >74.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row137_col0\" class=\"data row137 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row137_col1\" class=\"data row137 col1\" >21/09/12</td>\n",
       "      <td id=\"T_aa7d2_row137_col2\" class=\"data row137 col2\" >$ 1506126.06</td>\n",
       "      <td id=\"T_aa7d2_row137_col3\" class=\"data row137 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row137_col4\" class=\"data row137 col4\" >69.87º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row138_col0\" class=\"data row138 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row138_col1\" class=\"data row138 col1\" >28/09/12</td>\n",
       "      <td id=\"T_aa7d2_row138_col2\" class=\"data row138 col2\" >$ 1437059.26</td>\n",
       "      <td id=\"T_aa7d2_row138_col3\" class=\"data row138 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row138_col4\" class=\"data row138 col4\" >76.08º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row139_col0\" class=\"data row139 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row139_col1\" class=\"data row139 col1\" >10/05/12</td>\n",
       "      <td id=\"T_aa7d2_row139_col2\" class=\"data row139 col2\" >$ 1670785.97</td>\n",
       "      <td id=\"T_aa7d2_row139_col3\" class=\"data row139 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row139_col4\" class=\"data row139 col4\" >68.55º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row140_col0\" class=\"data row140 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row140_col1\" class=\"data row140 col1\" >10/12/12</td>\n",
       "      <td id=\"T_aa7d2_row140_col2\" class=\"data row140 col2\" >$ 1573072.81</td>\n",
       "      <td id=\"T_aa7d2_row140_col3\" class=\"data row140 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row140_col4\" class=\"data row140 col4\" >62.99º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row141_col0\" class=\"data row141 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row141_col1\" class=\"data row141 col1\" >19/10/12</td>\n",
       "      <td id=\"T_aa7d2_row141_col2\" class=\"data row141 col2\" >$ 1508068.77</td>\n",
       "      <td id=\"T_aa7d2_row141_col3\" class=\"data row141 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row141_col4\" class=\"data row141 col4\" >67.97º</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_aa7d2_row142_col0\" class=\"data row142 col0\" >1</td>\n",
       "      <td id=\"T_aa7d2_row142_col1\" class=\"data row142 col1\" >26/10/12</td>\n",
       "      <td id=\"T_aa7d2_row142_col2\" class=\"data row142 col2\" >$ 1493659.74</td>\n",
       "      <td id=\"T_aa7d2_row142_col3\" class=\"data row142 col3\" >False</td>\n",
       "      <td id=\"T_aa7d2_row142_col4\" class=\"data row142 col4\" >69.16º</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb51722a9a0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df_aux.style.format(format_dict)\n",
    "    .hide_index()\n",
    "    .highlight_max([\"feriado\"], color=\"red\")\n",
    "    .background_gradient(subset=\"vendas_semanais\", cmap=\"Greens\")\n",
    "    .bar('temperatura', color='lightblue', align='zero')\n",
    "    .set_caption('Vendas semanais das lojas do Walmart')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad2c43a",
   "metadata": {},
   "source": [
    "## <font color=green> Reindexação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "853e018a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed96462",
   "metadata": {},
   "source": [
    "- Reindex nessa Series reorganiza os dados de acordo com o novo índice, introduzindo valores indicativos de ausência se algum valor de índice não estava presente antes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ece1205c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])\n",
    "obj2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c7eecf",
   "metadata": {},
   "source": [
    "- Para dados ordenados, como séries temporais, talvez seja desejável fazer alguma interpolação ou preenchimento de valores de reindexação. A opção method nos permite fazer isso, usando um método como ffill, que faz um preenchimento para a frente (foward-fill) dos valores, enquanto bfill preenche pra trás"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0e721f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    purple\n",
       "4    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d6a39673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(6), method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2eabaf79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1    purple\n",
       "2    purple\n",
       "3    yellow\n",
       "4    yellow\n",
       "5       NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(6), method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc59cf2",
   "metadata": {},
   "source": [
    "- Com DataFrame, reindex pode alterar o índice (linha), as colunas, ou ambas. Se apenas uma sequência for passada, as linhas serão reindexadas no resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ed1716ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.arange(9).reshape((3,3)), \n",
    "                        index=['a','c','d'], \n",
    "                        columns=['Ohio', 'Texas', 'California'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "46269014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a   0.0    1.0         2.0\n",
       "b   NaN    NaN         NaN\n",
       "c   3.0    4.0         5.0\n",
       "d   6.0    7.0         8.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = frame.reindex(['a','b','c','d'])\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9d64e0d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3 = frame.reindex(columns=['Texas', 'Utah', 'California'])\n",
    "frame3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29291849",
   "metadata": {},
   "source": [
    "- Convertendo coluna multivalores para multiplas linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b6ec2d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>M</td>\n",
       "      <td>Writing,Chess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>Jack</td>\n",
       "      <td>M</td>\n",
       "      <td>Drawing,Coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>Music,Reading</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name sex         hobbies\n",
       "K  Kevin   M   Writing,Chess\n",
       "J   Jack   M  Drawing,Coding\n",
       "M   Mary   F   Music,Reading"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"name\": [\"Kevin\", \"Jack\", \"Mary\"],\n",
    "    \"sex\": [\"M\", \"M\", \"F\"],\n",
    "    \"hobbies\": [\"Writing,Chess\", \"Drawing,Coding\", \"Music,Reading\"]\n",
    "}, index=[\"K\", \"J\", \"M\"])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8d018ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hobbies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>M</td>\n",
       "      <td>[Writing, Chess]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>Jack</td>\n",
       "      <td>M</td>\n",
       "      <td>[Drawing, Coding]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>[Music, Reading]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name sex            hobbies\n",
       "K  Kevin   M   [Writing, Chess]\n",
       "J   Jack   M  [Drawing, Coding]\n",
       "M   Mary   F   [Music, Reading]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"hobbies\"] = df[\"hobbies\"].str.split(\",\") \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ae2e0edb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Jack</td>\n",
       "      <td>M</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>Jack</td>\n",
       "      <td>M</td>\n",
       "      <td>Coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>Reading</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    name sex  hobbies\n",
       "K  Kevin   M  Writing\n",
       "K  Kevin   M    Chess\n",
       "J   Jack   M  Drawing\n",
       "J   Jack   M   Coding\n",
       "M   Mary   F    Music\n",
       "M   Mary   F  Reading"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.explode(\"hobbies\") \n",
    "df"
   ]
  }
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