Aula_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula_2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOUFna/1+MBsWd0jeFVneoP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zaptudo/Alura-imersaodados3/blob/main/Aula_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivRxng-oCXDT"
      },
      "source": [
        "#Alura - Imersão em Ciência de Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtKS_1JpCSCK"
      },
      "source": [
        "##Exemplos de aula"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_BxAQHm4r1o"
      },
      "source": [
        "###Bibliotecas usadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-mmNWBOv2In"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5l5aWc4v4mW5"
      },
      "source": [
        "###Importando os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07yYKndJwuWy"
      },
      "source": [
        "dados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true', compression='zip')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "Sn4FLpfTw12j",
        "outputId": "36c864ae-95c6-4466-b865-7624450b7173"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>...</th>\n",
              "      <th>c-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HkIv7Qz52zeA"
      },
      "source": [
        "###Renomeando variável 'droga' para 'composto'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqN7zZMpw45U"
      },
      "source": [
        "dados.rename(columns={'droga': 'composto'}, inplace=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "YBEH2UKDxEFC",
        "outputId": "c0dc0342-b52f-4d56-9259-65c5e4a623f9"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>...</th>\n",
              "      <th>c-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OolTHxxd2sBw"
      },
      "source": [
        "###Os 5 compostos mais usados são:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMYK4GBnx8In"
      },
      "source": [
        "cod_compostos = dados['composto'].value_counts()[:5].index"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQShThpOyjH1"
      },
      "source": [
        "dados_compostos = dados.query(expr='composto in @cod_compostos')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "khDQlOOrxHUx",
        "outputId": "61c1d32b-c8d1-4749-b3cf-fe47e7bf3424"
      },
      "source": [
        "# Define o estilo padrão do seaborn\n",
        "sns.set()\n",
        "\n",
        "# Configura o tamanho do gráfico\n",
        "plt.figure(figsize=[10, 8])\n",
        "\n",
        "ax = sns.countplot(x='composto', data=dados_compostos)\n",
        "ax.set_title('Compostos mais usados', fontsize=25)\n",
        "ax.set(xlabel='Código do composto', ylabel='Frequencia')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l84TyBcJCyOL"
      },
      "source": [
        "###Quantos elementos únicos a variável *g-0* possui"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCg8RbaMChiF",
        "outputId": "5e9b7fee-82a5-410d-e655-faffef2cdf40"
      },
      "source": [
        "len(dados['g-0'].unique())"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14367"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWc7Wgz5DHTt"
      },
      "source": [
        "###Quais os valores mínimo e máximo da variável *g-0*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYtZdJOBDGbb",
        "outputId": "313c7d7b-5c49-4153-94e1-a69c9cc56bf1"
      },
      "source": [
        "dados['g-0'].min()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-5.513"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNnPZO1aDBo_",
        "outputId": "dd71b8df-a79b-4189-920c-ae9e10d3d780"
      },
      "source": [
        "dados['g-0'].max()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDt2h-GpDgLC"
      },
      "source": [
        "###Histograma da variável *g-0*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j46dN5J3F-NQ"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "moYJF4cPDf46",
        "outputId": "c5da8285-386b-41dd-eb27-536ac41382d3"
      },
      "source": [
        "# bins é o número de classes\n",
        "dados['g-0'].hist(bins=100)\n",
        "\n",
        "# a curva parece uma curva normal"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab100f310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vb1w8hbEHMeu"
      },
      "source": [
        "###Média da variável *g-0*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpvEPZwlFYn6",
        "outputId": "4a63e10f-b545-4474-8ad7-5a854befbf25"
      },
      "source": [
        "dados['g-0'].mean()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24836629293692816"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJGeNGOUI3w3"
      },
      "source": [
        "###Medidas resumo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "DR5OQwaRHZ2P",
        "outputId": "c9f0754e-31d6-4c6b-e842-a1795a2123f2"
      },
      "source": [
        "dados.describe()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tempo</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>g-36</th>\n",
              "      <th>g-37</th>\n",
              "      <th>g-38</th>\n",
              "      <th>...</th>\n",
              "      <th>c-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>48.020156</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>-0.138836</td>\n",
              "      <td>0.035961</td>\n",
              "      <td>-0.202651</td>\n",
              "      <td>-0.190083</td>\n",
              "      <td>0.119905</td>\n",
              "      <td>-0.123321</td>\n",
              "      <td>0.182307</td>\n",
              "      <td>0.143203</td>\n",
              "      <td>0.209402</td>\n",
              "      <td>-0.173884</td>\n",
              "      <td>-0.024432</td>\n",
              "      <td>0.126823</td>\n",
              "      <td>-0.146663</td>\n",
              "      <td>0.087687</td>\n",
              "      <td>-0.082982</td>\n",
              "      <td>-0.111908</td>\n",
              "      <td>-0.087379</td>\n",
              "      <td>0.047548</td>\n",
              "      <td>-0.117474</td>\n",
              "      <td>-0.113212</td>\n",
              "      <td>-0.052746</td>\n",
              "      <td>-0.091055</td>\n",
              "      <td>0.112176</td>\n",
              "      <td>-0.046458</td>\n",
              "      <td>-0.076239</td>\n",
              "      <td>-0.197699</td>\n",
              "      <td>0.382177</td>\n",
              "      <td>-0.189432</td>\n",
              "      <td>0.078791</td>\n",
              "      <td>-0.093312</td>\n",
              "      <td>0.135729</td>\n",
              "      <td>-0.188616</td>\n",
              "      <td>-0.606710</td>\n",
              "      <td>0.534425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.517397</td>\n",
              "      <td>-0.360770</td>\n",
              "      <td>-0.435752</td>\n",
              "      <td>-0.613591</td>\n",
              "      <td>-0.402083</td>\n",
              "      <td>-0.619682</td>\n",
              "      <td>-0.452265</td>\n",
              "      <td>-0.497164</td>\n",
              "      <td>-0.413836</td>\n",
              "      <td>-0.277029</td>\n",
              "      <td>-0.547845</td>\n",
              "      <td>-0.358611</td>\n",
              "      <td>-0.442906</td>\n",
              "      <td>-0.475194</td>\n",
              "      <td>-0.010404</td>\n",
              "      <td>-0.467001</td>\n",
              "      <td>-0.276963</td>\n",
              "      <td>-0.455848</td>\n",
              "      <td>-0.412918</td>\n",
              "      <td>-0.456404</td>\n",
              "      <td>-0.472514</td>\n",
              "      <td>-0.505481</td>\n",
              "      <td>-0.492735</td>\n",
              "      <td>-0.446836</td>\n",
              "      <td>-0.463029</td>\n",
              "      <td>-0.409310</td>\n",
              "      <td>-0.333124</td>\n",
              "      <td>-0.295009</td>\n",
              "      <td>-0.328342</td>\n",
              "      <td>-0.401615</td>\n",
              "      <td>-0.469244</td>\n",
              "      <td>-0.461411</td>\n",
              "      <td>-0.513256</td>\n",
              "      <td>-0.500142</td>\n",
              "      <td>-0.507093</td>\n",
              "      <td>-0.353726</td>\n",
              "      <td>-0.463485</td>\n",
              "      <td>-0.378241</td>\n",
              "      <td>-0.470252</td>\n",
              "      <td>-0.301505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19.402807</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>1.179388</td>\n",
              "      <td>0.882395</td>\n",
              "      <td>1.125494</td>\n",
              "      <td>1.749885</td>\n",
              "      <td>1.087180</td>\n",
              "      <td>1.291501</td>\n",
              "      <td>1.253604</td>\n",
              "      <td>1.234590</td>\n",
              "      <td>1.273068</td>\n",
              "      <td>1.247178</td>\n",
              "      <td>0.659839</td>\n",
              "      <td>1.418997</td>\n",
              "      <td>1.179688</td>\n",
              "      <td>0.743301</td>\n",
              "      <td>0.844796</td>\n",
              "      <td>1.219529</td>\n",
              "      <td>0.824401</td>\n",
              "      <td>0.924838</td>\n",
              "      <td>0.760159</td>\n",
              "      <td>1.203186</td>\n",
              "      <td>0.866977</td>\n",
              "      <td>1.103765</td>\n",
              "      <td>1.001687</td>\n",
              "      <td>1.027758</td>\n",
              "      <td>1.279399</td>\n",
              "      <td>1.302567</td>\n",
              "      <td>1.559174</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>1.172270</td>\n",
              "      <td>1.174325</td>\n",
              "      <td>1.061719</td>\n",
              "      <td>1.397677</td>\n",
              "      <td>2.200277</td>\n",
              "      <td>2.003317</td>\n",
              "      <td>...</td>\n",
              "      <td>2.122318</td>\n",
              "      <td>1.710725</td>\n",
              "      <td>1.898871</td>\n",
              "      <td>2.307820</td>\n",
              "      <td>1.785055</td>\n",
              "      <td>2.225596</td>\n",
              "      <td>1.991021</td>\n",
              "      <td>2.063896</td>\n",
              "      <td>1.887001</td>\n",
              "      <td>1.459639</td>\n",
              "      <td>2.187835</td>\n",
              "      <td>1.730634</td>\n",
              "      <td>1.924716</td>\n",
              "      <td>2.021927</td>\n",
              "      <td>1.029820</td>\n",
              "      <td>2.004317</td>\n",
              "      <td>1.429340</td>\n",
              "      <td>1.924263</td>\n",
              "      <td>1.888788</td>\n",
              "      <td>1.832863</td>\n",
              "      <td>2.011396</td>\n",
              "      <td>2.091353</td>\n",
              "      <td>2.055624</td>\n",
              "      <td>1.987476</td>\n",
              "      <td>2.014045</td>\n",
              "      <td>1.883974</td>\n",
              "      <td>1.647241</td>\n",
              "      <td>1.634073</td>\n",
              "      <td>1.663170</td>\n",
              "      <td>1.832794</td>\n",
              "      <td>2.000488</td>\n",
              "      <td>2.042475</td>\n",
              "      <td>2.001714</td>\n",
              "      <td>2.107105</td>\n",
              "      <td>2.159589</td>\n",
              "      <td>1.629291</td>\n",
              "      <td>2.059725</td>\n",
              "      <td>1.703615</td>\n",
              "      <td>1.834828</td>\n",
              "      <td>1.407918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>-5.513000</td>\n",
              "      <td>-5.737000</td>\n",
              "      <td>-9.104000</td>\n",
              "      <td>-5.998000</td>\n",
              "      <td>-6.369000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.337000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.870000</td>\n",
              "      <td>-8.587000</td>\n",
              "      <td>-5.018000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.226000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.700000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.272000</td>\n",
              "      <td>-8.184000</td>\n",
              "      <td>-4.835000</td>\n",
              "      <td>-7.913000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.956000</td>\n",
              "      <td>-8.356000</td>\n",
              "      <td>-7.182000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.261000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.839000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-6.452000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.938000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.602225</td>\n",
              "      <td>-0.493900</td>\n",
              "      <td>-0.525175</td>\n",
              "      <td>-0.511675</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.511475</td>\n",
              "      <td>-0.489675</td>\n",
              "      <td>-0.447500</td>\n",
              "      <td>-0.481200</td>\n",
              "      <td>-0.607975</td>\n",
              "      <td>-0.404150</td>\n",
              "      <td>-0.391950</td>\n",
              "      <td>-0.513775</td>\n",
              "      <td>-0.272200</td>\n",
              "      <td>-0.488675</td>\n",
              "      <td>-0.524600</td>\n",
              "      <td>-0.538900</td>\n",
              "      <td>-0.440375</td>\n",
              "      <td>-0.508900</td>\n",
              "      <td>-0.533900</td>\n",
              "      <td>-0.497700</td>\n",
              "      <td>-0.512875</td>\n",
              "      <td>-0.467800</td>\n",
              "      <td>-0.378300</td>\n",
              "      <td>-0.505750</td>\n",
              "      <td>-0.457975</td>\n",
              "      <td>-0.328200</td>\n",
              "      <td>-0.600500</td>\n",
              "      <td>-0.478700</td>\n",
              "      <td>-0.570525</td>\n",
              "      <td>-0.481800</td>\n",
              "      <td>-0.541950</td>\n",
              "      <td>-0.604100</td>\n",
              "      <td>-0.470250</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.588075</td>\n",
              "      <td>-0.564025</td>\n",
              "      <td>-0.561000</td>\n",
              "      <td>-0.583250</td>\n",
              "      <td>-0.566500</td>\n",
              "      <td>-0.603200</td>\n",
              "      <td>-0.541575</td>\n",
              "      <td>-0.560825</td>\n",
              "      <td>-0.555200</td>\n",
              "      <td>-0.534500</td>\n",
              "      <td>-0.569100</td>\n",
              "      <td>-0.558300</td>\n",
              "      <td>-0.573350</td>\n",
              "      <td>-0.594275</td>\n",
              "      <td>-0.389925</td>\n",
              "      <td>-0.551200</td>\n",
              "      <td>-0.544150</td>\n",
              "      <td>-0.575075</td>\n",
              "      <td>-0.568275</td>\n",
              "      <td>-0.582650</td>\n",
              "      <td>-0.558575</td>\n",
              "      <td>-0.562375</td>\n",
              "      <td>-0.572800</td>\n",
              "      <td>-0.561225</td>\n",
              "      <td>-0.560675</td>\n",
              "      <td>-0.560100</td>\n",
              "      <td>-0.533700</td>\n",
              "      <td>-0.504575</td>\n",
              "      <td>-0.544275</td>\n",
              "      <td>-0.569150</td>\n",
              "      <td>-0.566175</td>\n",
              "      <td>-0.565975</td>\n",
              "      <td>-0.589975</td>\n",
              "      <td>-0.568700</td>\n",
              "      <td>-0.563775</td>\n",
              "      <td>-0.567975</td>\n",
              "      <td>-0.552575</td>\n",
              "      <td>-0.561000</td>\n",
              "      <td>-0.592600</td>\n",
              "      <td>-0.562900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>-0.008850</td>\n",
              "      <td>-0.046600</td>\n",
              "      <td>0.075200</td>\n",
              "      <td>0.008050</td>\n",
              "      <td>-0.026900</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>-0.017900</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.160450</td>\n",
              "      <td>0.038550</td>\n",
              "      <td>0.013800</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>-0.030100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.149400</td>\n",
              "      <td>-0.002200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.027800</td>\n",
              "      <td>-0.002800</td>\n",
              "      <td>-0.069350</td>\n",
              "      <td>-0.011800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018300</td>\n",
              "      <td>-0.011650</td>\n",
              "      <td>0.016100</td>\n",
              "      <td>0.037600</td>\n",
              "      <td>0.003750</td>\n",
              "      <td>0.021750</td>\n",
              "      <td>0.027000</td>\n",
              "      <td>0.019100</td>\n",
              "      <td>-0.054000</td>\n",
              "      <td>0.023350</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>-0.000600</td>\n",
              "      <td>0.015350</td>\n",
              "      <td>-0.000700</td>\n",
              "      <td>0.005800</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.017650</td>\n",
              "      <td>-0.041550</td>\n",
              "      <td>-0.002950</td>\n",
              "      <td>-0.012650</td>\n",
              "      <td>-0.005600</td>\n",
              "      <td>0.007650</td>\n",
              "      <td>0.004950</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.023800</td>\n",
              "      <td>-0.011450</td>\n",
              "      <td>-0.007100</td>\n",
              "      <td>-0.019500</td>\n",
              "      <td>-0.019500</td>\n",
              "      <td>-0.009300</td>\n",
              "      <td>0.081550</td>\n",
              "      <td>-0.006900</td>\n",
              "      <td>0.018400</td>\n",
              "      <td>-0.014650</td>\n",
              "      <td>-0.014350</td>\n",
              "      <td>0.005300</td>\n",
              "      <td>-0.005300</td>\n",
              "      <td>-0.004050</td>\n",
              "      <td>0.003300</td>\n",
              "      <td>-0.007900</td>\n",
              "      <td>-0.004600</td>\n",
              "      <td>-0.002400</td>\n",
              "      <td>0.007850</td>\n",
              "      <td>-0.005600</td>\n",
              "      <td>-0.020600</td>\n",
              "      <td>-0.030000</td>\n",
              "      <td>-0.009900</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>-0.009100</td>\n",
              "      <td>-0.013750</td>\n",
              "      <td>-0.003300</td>\n",
              "      <td>-0.010250</td>\n",
              "      <td>-0.001250</td>\n",
              "      <td>-0.006800</td>\n",
              "      <td>0.014000</td>\n",
              "      <td>-0.019500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>0.510425</td>\n",
              "      <td>0.528725</td>\n",
              "      <td>0.411900</td>\n",
              "      <td>0.549225</td>\n",
              "      <td>0.697775</td>\n",
              "      <td>0.525400</td>\n",
              "      <td>0.575275</td>\n",
              "      <td>0.604450</td>\n",
              "      <td>0.575825</td>\n",
              "      <td>0.457975</td>\n",
              "      <td>0.382475</td>\n",
              "      <td>0.829500</td>\n",
              "      <td>0.494775</td>\n",
              "      <td>0.327800</td>\n",
              "      <td>0.400600</td>\n",
              "      <td>0.492400</td>\n",
              "      <td>0.414875</td>\n",
              "      <td>0.433400</td>\n",
              "      <td>0.329250</td>\n",
              "      <td>0.527700</td>\n",
              "      <td>0.461650</td>\n",
              "      <td>0.508425</td>\n",
              "      <td>0.586450</td>\n",
              "      <td>0.431275</td>\n",
              "      <td>0.507600</td>\n",
              "      <td>0.458075</td>\n",
              "      <td>0.471075</td>\n",
              "      <td>0.391950</td>\n",
              "      <td>0.551300</td>\n",
              "      <td>0.503725</td>\n",
              "      <td>0.564875</td>\n",
              "      <td>0.517025</td>\n",
              "      <td>0.460500</td>\n",
              "      <td>0.642300</td>\n",
              "      <td>...</td>\n",
              "      <td>0.452675</td>\n",
              "      <td>0.427675</td>\n",
              "      <td>0.462175</td>\n",
              "      <td>0.447975</td>\n",
              "      <td>0.447150</td>\n",
              "      <td>0.441250</td>\n",
              "      <td>0.470600</td>\n",
              "      <td>0.458550</td>\n",
              "      <td>0.441000</td>\n",
              "      <td>0.460075</td>\n",
              "      <td>0.460950</td>\n",
              "      <td>0.449975</td>\n",
              "      <td>0.445200</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.563575</td>\n",
              "      <td>0.456350</td>\n",
              "      <td>0.493400</td>\n",
              "      <td>0.448375</td>\n",
              "      <td>0.451975</td>\n",
              "      <td>0.463075</td>\n",
              "      <td>0.447675</td>\n",
              "      <td>0.462000</td>\n",
              "      <td>0.468900</td>\n",
              "      <td>0.452375</td>\n",
              "      <td>0.460475</td>\n",
              "      <td>0.461675</td>\n",
              "      <td>0.465950</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.450075</td>\n",
              "      <td>0.430875</td>\n",
              "      <td>0.457750</td>\n",
              "      <td>0.461500</td>\n",
              "      <td>0.445675</td>\n",
              "      <td>0.452900</td>\n",
              "      <td>0.470900</td>\n",
              "      <td>0.444750</td>\n",
              "      <td>0.465225</td>\n",
              "      <td>0.446400</td>\n",
              "      <td>0.461275</td>\n",
              "      <td>0.438650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.039000</td>\n",
              "      <td>8.257000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.282000</td>\n",
              "      <td>7.333000</td>\n",
              "      <td>5.473000</td>\n",
              "      <td>8.887000</td>\n",
              "      <td>6.433000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.134000</td>\n",
              "      <td>6.418000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>8.872000</td>\n",
              "      <td>4.081000</td>\n",
              "      <td>9.842000</td>\n",
              "      <td>5.248000</td>\n",
              "      <td>5.942000</td>\n",
              "      <td>5.201000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.494000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.416000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.796000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.834000</td>\n",
              "      <td>5.602000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.888000</td>\n",
              "      <td>3.596000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>3.549000</td>\n",
              "      <td>3.382000</td>\n",
              "      <td>3.328000</td>\n",
              "      <td>4.157000</td>\n",
              "      <td>3.736000</td>\n",
              "      <td>3.582000</td>\n",
              "      <td>3.119000</td>\n",
              "      <td>3.323000</td>\n",
              "      <td>5.014000</td>\n",
              "      <td>2.898000</td>\n",
              "      <td>4.185000</td>\n",
              "      <td>3.170000</td>\n",
              "      <td>3.276000</td>\n",
              "      <td>4.992000</td>\n",
              "      <td>3.770000</td>\n",
              "      <td>2.851000</td>\n",
              "      <td>3.211000</td>\n",
              "      <td>4.534000</td>\n",
              "      <td>3.890000</td>\n",
              "      <td>3.994000</td>\n",
              "      <td>4.321000</td>\n",
              "      <td>4.020000</td>\n",
              "      <td>3.738000</td>\n",
              "      <td>3.252000</td>\n",
              "      <td>5.406000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>3.320000</td>\n",
              "      <td>4.069000</td>\n",
              "      <td>3.960000</td>\n",
              "      <td>3.927000</td>\n",
              "      <td>3.596000</td>\n",
              "      <td>3.747000</td>\n",
              "      <td>2.814000</td>\n",
              "      <td>3.505000</td>\n",
              "      <td>2.924000</td>\n",
              "      <td>3.111000</td>\n",
              "      <td>3.805000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 873 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              tempo           g-0  ...          c-98          c-99\n",
              "count  23814.000000  23814.000000  ...  23814.000000  23814.000000\n",
              "mean      48.020156      0.248366  ...     -0.470252     -0.301505\n",
              "std       19.402807      1.393399  ...      1.834828      1.407918\n",
              "min       24.000000     -5.513000  ...    -10.000000    -10.000000\n",
              "25%       24.000000     -0.473075  ...     -0.592600     -0.562900\n",
              "50%       48.000000     -0.008850  ...      0.014000     -0.019500\n",
              "75%       72.000000      0.525700  ...      0.461275      0.438650\n",
              "max       72.000000     10.000000  ...      3.111000      3.805000\n",
              "\n",
              "[8 rows x 873 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JIzd4riI7u0"
      },
      "source": [
        "###Seleção de intervalos de linhas e colunas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "gkhL3R43Hy2W",
        "outputId": "ee2f3db6-b5d2-4785-8fa6-d16ae8cd6cd2"
      },
      "source": [
        "dados[['g-0', 'g-1']]"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>g-0</th>\n",
              "      <th>g-1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-0     g-1\n",
              "0      1.0620  0.5577\n",
              "1      0.0743  0.4087\n",
              "2      0.6280  0.5817\n",
              "3     -0.5138 -0.2491\n",
              "4     -0.3254 -0.4009\n",
              "...       ...     ...\n",
              "23809  0.1394 -0.0636\n",
              "23810 -1.3260  0.3478\n",
              "23811  0.3942  0.3756\n",
              "23812  0.6660  0.2324\n",
              "23813 -0.8598  1.0240\n",
              "\n",
              "[23814 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "xll51CAQH_rW",
        "outputId": "4b6685bf-caef-454c-f463-5be83bc2db85"
      },
      "source": [
        "# loc[intervalo de linhas, intervalo de colunas]\n",
        "dados.loc[0:,'g-0':'g-771'].describe()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>g-36</th>\n",
              "      <th>g-37</th>\n",
              "      <th>g-38</th>\n",
              "      <th>g-39</th>\n",
              "      <th>...</th>\n",
              "      <th>g-732</th>\n",
              "      <th>g-733</th>\n",
              "      <th>g-734</th>\n",
              "      <th>g-735</th>\n",
              "      <th>g-736</th>\n",
              "      <th>g-737</th>\n",
              "      <th>g-738</th>\n",
              "      <th>g-739</th>\n",
              "      <th>g-740</th>\n",
              "      <th>g-741</th>\n",
              "      <th>g-742</th>\n",
              "      <th>g-743</th>\n",
              "      <th>g-744</th>\n",
              "      <th>g-745</th>\n",
              "      <th>g-746</th>\n",
              "      <th>g-747</th>\n",
              "      <th>g-748</th>\n",
              "      <th>g-749</th>\n",
              "      <th>g-750</th>\n",
              "      <th>g-751</th>\n",
              "      <th>g-752</th>\n",
              "      <th>g-753</th>\n",
              "      <th>g-754</th>\n",
              "      <th>g-755</th>\n",
              "      <th>g-756</th>\n",
              "      <th>g-757</th>\n",
              "      <th>g-758</th>\n",
              "      <th>g-759</th>\n",
              "      <th>g-760</th>\n",
              "      <th>g-761</th>\n",
              "      <th>g-762</th>\n",
              "      <th>g-763</th>\n",
              "      <th>g-764</th>\n",
              "      <th>g-765</th>\n",
              "      <th>g-766</th>\n",
              "      <th>g-767</th>\n",
              "      <th>g-768</th>\n",
              "      <th>g-769</th>\n",
              "      <th>g-770</th>\n",
              "      <th>g-771</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.248366</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>-0.138836</td>\n",
              "      <td>0.035961</td>\n",
              "      <td>-0.202651</td>\n",
              "      <td>-0.190083</td>\n",
              "      <td>0.119905</td>\n",
              "      <td>-0.123321</td>\n",
              "      <td>0.182307</td>\n",
              "      <td>0.143203</td>\n",
              "      <td>0.209402</td>\n",
              "      <td>-0.173884</td>\n",
              "      <td>-0.024432</td>\n",
              "      <td>0.126823</td>\n",
              "      <td>-0.146663</td>\n",
              "      <td>0.087687</td>\n",
              "      <td>-0.082982</td>\n",
              "      <td>-0.111908</td>\n",
              "      <td>-0.087379</td>\n",
              "      <td>0.047548</td>\n",
              "      <td>-0.117474</td>\n",
              "      <td>-0.113212</td>\n",
              "      <td>-0.052746</td>\n",
              "      <td>-0.091055</td>\n",
              "      <td>0.112176</td>\n",
              "      <td>-0.046458</td>\n",
              "      <td>-0.076239</td>\n",
              "      <td>-0.197699</td>\n",
              "      <td>0.382177</td>\n",
              "      <td>-0.189432</td>\n",
              "      <td>0.078791</td>\n",
              "      <td>-0.093312</td>\n",
              "      <td>0.135729</td>\n",
              "      <td>-0.188616</td>\n",
              "      <td>-0.606710</td>\n",
              "      <td>0.534425</td>\n",
              "      <td>0.132026</td>\n",
              "      <td>...</td>\n",
              "      <td>0.263569</td>\n",
              "      <td>-0.242638</td>\n",
              "      <td>0.110180</td>\n",
              "      <td>-0.039868</td>\n",
              "      <td>-0.064056</td>\n",
              "      <td>0.193629</td>\n",
              "      <td>0.004470</td>\n",
              "      <td>-0.187085</td>\n",
              "      <td>0.067186</td>\n",
              "      <td>0.006159</td>\n",
              "      <td>-0.201189</td>\n",
              "      <td>-0.243317</td>\n",
              "      <td>0.548903</td>\n",
              "      <td>0.247065</td>\n",
              "      <td>0.085950</td>\n",
              "      <td>0.090544</td>\n",
              "      <td>0.215525</td>\n",
              "      <td>-0.007705</td>\n",
              "      <td>0.236954</td>\n",
              "      <td>0.022785</td>\n",
              "      <td>0.103224</td>\n",
              "      <td>0.048402</td>\n",
              "      <td>0.109367</td>\n",
              "      <td>-0.103262</td>\n",
              "      <td>0.004129</td>\n",
              "      <td>0.066211</td>\n",
              "      <td>0.072773</td>\n",
              "      <td>-0.033051</td>\n",
              "      <td>-0.400643</td>\n",
              "      <td>-0.338978</td>\n",
              "      <td>0.207299</td>\n",
              "      <td>0.009161</td>\n",
              "      <td>0.171027</td>\n",
              "      <td>0.002316</td>\n",
              "      <td>-0.209799</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>0.101524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.393399</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>1.179388</td>\n",
              "      <td>0.882395</td>\n",
              "      <td>1.125494</td>\n",
              "      <td>1.749885</td>\n",
              "      <td>1.087180</td>\n",
              "      <td>1.291501</td>\n",
              "      <td>1.253604</td>\n",
              "      <td>1.234590</td>\n",
              "      <td>1.273068</td>\n",
              "      <td>1.247178</td>\n",
              "      <td>0.659839</td>\n",
              "      <td>1.418997</td>\n",
              "      <td>1.179688</td>\n",
              "      <td>0.743301</td>\n",
              "      <td>0.844796</td>\n",
              "      <td>1.219529</td>\n",
              "      <td>0.824401</td>\n",
              "      <td>0.924838</td>\n",
              "      <td>0.760159</td>\n",
              "      <td>1.203186</td>\n",
              "      <td>0.866977</td>\n",
              "      <td>1.103765</td>\n",
              "      <td>1.001687</td>\n",
              "      <td>1.027758</td>\n",
              "      <td>1.279399</td>\n",
              "      <td>1.302567</td>\n",
              "      <td>1.559174</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>1.172270</td>\n",
              "      <td>1.174325</td>\n",
              "      <td>1.061719</td>\n",
              "      <td>1.397677</td>\n",
              "      <td>2.200277</td>\n",
              "      <td>2.003317</td>\n",
              "      <td>0.990477</td>\n",
              "      <td>...</td>\n",
              "      <td>1.075809</td>\n",
              "      <td>1.309482</td>\n",
              "      <td>0.881283</td>\n",
              "      <td>0.917119</td>\n",
              "      <td>1.186966</td>\n",
              "      <td>1.212621</td>\n",
              "      <td>1.175611</td>\n",
              "      <td>1.182180</td>\n",
              "      <td>1.057292</td>\n",
              "      <td>0.996940</td>\n",
              "      <td>1.029370</td>\n",
              "      <td>1.378815</td>\n",
              "      <td>2.023791</td>\n",
              "      <td>1.639307</td>\n",
              "      <td>1.238753</td>\n",
              "      <td>0.961212</td>\n",
              "      <td>1.293783</td>\n",
              "      <td>0.966213</td>\n",
              "      <td>1.284074</td>\n",
              "      <td>1.015557</td>\n",
              "      <td>1.035968</td>\n",
              "      <td>1.048645</td>\n",
              "      <td>0.856080</td>\n",
              "      <td>1.175372</td>\n",
              "      <td>0.701031</td>\n",
              "      <td>0.996914</td>\n",
              "      <td>1.077310</td>\n",
              "      <td>1.579948</td>\n",
              "      <td>1.913468</td>\n",
              "      <td>1.701554</td>\n",
              "      <td>1.096488</td>\n",
              "      <td>1.200395</td>\n",
              "      <td>1.357701</td>\n",
              "      <td>1.052771</td>\n",
              "      <td>1.201918</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>1.417674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.513000</td>\n",
              "      <td>-5.737000</td>\n",
              "      <td>-9.104000</td>\n",
              "      <td>-5.998000</td>\n",
              "      <td>-6.369000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.337000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.870000</td>\n",
              "      <td>-8.587000</td>\n",
              "      <td>-5.018000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.226000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.700000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.272000</td>\n",
              "      <td>-8.184000</td>\n",
              "      <td>-4.835000</td>\n",
              "      <td>-7.913000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.956000</td>\n",
              "      <td>-8.356000</td>\n",
              "      <td>-7.182000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.261000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.223000</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.504000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-3.790000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-7.920000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.462000</td>\n",
              "      <td>-6.634000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.912000</td>\n",
              "      <td>-9.833000</td>\n",
              "      <td>-7.227000</td>\n",
              "      <td>-5.561000</td>\n",
              "      <td>-8.393000</td>\n",
              "      <td>-5.790000</td>\n",
              "      <td>-4.915000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-7.918000</td>\n",
              "      <td>-5.255000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-6.264000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.505000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.193000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.795000</td>\n",
              "      <td>-8.180000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.269000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.602225</td>\n",
              "      <td>-0.493900</td>\n",
              "      <td>-0.525175</td>\n",
              "      <td>-0.511675</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.511475</td>\n",
              "      <td>-0.489675</td>\n",
              "      <td>-0.447500</td>\n",
              "      <td>-0.481200</td>\n",
              "      <td>-0.607975</td>\n",
              "      <td>-0.404150</td>\n",
              "      <td>-0.391950</td>\n",
              "      <td>-0.513775</td>\n",
              "      <td>-0.272200</td>\n",
              "      <td>-0.488675</td>\n",
              "      <td>-0.524600</td>\n",
              "      <td>-0.538900</td>\n",
              "      <td>-0.440375</td>\n",
              "      <td>-0.508900</td>\n",
              "      <td>-0.533900</td>\n",
              "      <td>-0.497700</td>\n",
              "      <td>-0.512875</td>\n",
              "      <td>-0.467800</td>\n",
              "      <td>-0.378300</td>\n",
              "      <td>-0.505750</td>\n",
              "      <td>-0.457975</td>\n",
              "      <td>-0.328200</td>\n",
              "      <td>-0.600500</td>\n",
              "      <td>-0.478700</td>\n",
              "      <td>-0.570525</td>\n",
              "      <td>-0.481800</td>\n",
              "      <td>-0.541950</td>\n",
              "      <td>-0.604100</td>\n",
              "      <td>-0.470250</td>\n",
              "      <td>-0.401700</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.390700</td>\n",
              "      <td>-0.565075</td>\n",
              "      <td>-0.433275</td>\n",
              "      <td>-0.498650</td>\n",
              "      <td>-0.526300</td>\n",
              "      <td>-0.430750</td>\n",
              "      <td>-0.488775</td>\n",
              "      <td>-0.593125</td>\n",
              "      <td>-0.498275</td>\n",
              "      <td>-0.489800</td>\n",
              "      <td>-0.564100</td>\n",
              "      <td>-0.538000</td>\n",
              "      <td>-0.464000</td>\n",
              "      <td>-0.521975</td>\n",
              "      <td>-0.488600</td>\n",
              "      <td>-0.442925</td>\n",
              "      <td>-0.457200</td>\n",
              "      <td>-0.477900</td>\n",
              "      <td>-0.458175</td>\n",
              "      <td>-0.479075</td>\n",
              "      <td>-0.478825</td>\n",
              "      <td>-0.489300</td>\n",
              "      <td>-0.327500</td>\n",
              "      <td>-0.576775</td>\n",
              "      <td>-0.278275</td>\n",
              "      <td>-0.492700</td>\n",
              "      <td>-0.501575</td>\n",
              "      <td>-0.526450</td>\n",
              "      <td>-0.507375</td>\n",
              "      <td>-0.559175</td>\n",
              "      <td>-0.433875</td>\n",
              "      <td>-0.502250</td>\n",
              "      <td>-0.537075</td>\n",
              "      <td>-0.508775</td>\n",
              "      <td>-0.606175</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>-0.523800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.008850</td>\n",
              "      <td>-0.046600</td>\n",
              "      <td>0.075200</td>\n",
              "      <td>0.008050</td>\n",
              "      <td>-0.026900</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>-0.017900</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.160450</td>\n",
              "      <td>0.038550</td>\n",
              "      <td>0.013800</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>-0.030100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.149400</td>\n",
              "      <td>-0.002200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.027800</td>\n",
              "      <td>-0.002800</td>\n",
              "      <td>-0.069350</td>\n",
              "      <td>-0.011800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018300</td>\n",
              "      <td>-0.011650</td>\n",
              "      <td>0.016100</td>\n",
              "      <td>0.037600</td>\n",
              "      <td>0.003750</td>\n",
              "      <td>0.021750</td>\n",
              "      <td>0.027000</td>\n",
              "      <td>0.019100</td>\n",
              "      <td>-0.054000</td>\n",
              "      <td>0.023350</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>-0.000600</td>\n",
              "      <td>0.015350</td>\n",
              "      <td>-0.000700</td>\n",
              "      <td>0.005800</td>\n",
              "      <td>0.007400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.065650</td>\n",
              "      <td>-0.014100</td>\n",
              "      <td>0.030850</td>\n",
              "      <td>-0.001800</td>\n",
              "      <td>-0.008400</td>\n",
              "      <td>0.101400</td>\n",
              "      <td>0.033400</td>\n",
              "      <td>-0.001600</td>\n",
              "      <td>0.000850</td>\n",
              "      <td>0.014800</td>\n",
              "      <td>-0.044000</td>\n",
              "      <td>0.017400</td>\n",
              "      <td>0.000850</td>\n",
              "      <td>-0.015550</td>\n",
              "      <td>0.025350</td>\n",
              "      <td>0.017750</td>\n",
              "      <td>0.053450</td>\n",
              "      <td>0.027700</td>\n",
              "      <td>-0.002100</td>\n",
              "      <td>0.009500</td>\n",
              "      <td>-0.004450</td>\n",
              "      <td>-0.000050</td>\n",
              "      <td>0.002400</td>\n",
              "      <td>0.024250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005300</td>\n",
              "      <td>0.013600</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.007350</td>\n",
              "      <td>0.038700</td>\n",
              "      <td>0.059600</td>\n",
              "      <td>-0.005900</td>\n",
              "      <td>-0.013600</td>\n",
              "      <td>-0.027700</td>\n",
              "      <td>-0.011850</td>\n",
              "      <td>0.009900</td>\n",
              "      <td>0.005400</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>0.028700</td>\n",
              "      <td>-0.006500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.525700</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>0.510425</td>\n",
              "      <td>0.528725</td>\n",
              "      <td>0.411900</td>\n",
              "      <td>0.549225</td>\n",
              "      <td>0.697775</td>\n",
              "      <td>0.525400</td>\n",
              "      <td>0.575275</td>\n",
              "      <td>0.604450</td>\n",
              "      <td>0.575825</td>\n",
              "      <td>0.457975</td>\n",
              "      <td>0.382475</td>\n",
              "      <td>0.829500</td>\n",
              "      <td>0.494775</td>\n",
              "      <td>0.327800</td>\n",
              "      <td>0.400600</td>\n",
              "      <td>0.492400</td>\n",
              "      <td>0.414875</td>\n",
              "      <td>0.433400</td>\n",
              "      <td>0.329250</td>\n",
              "      <td>0.527700</td>\n",
              "      <td>0.461650</td>\n",
              "      <td>0.508425</td>\n",
              "      <td>0.586450</td>\n",
              "      <td>0.431275</td>\n",
              "      <td>0.507600</td>\n",
              "      <td>0.458075</td>\n",
              "      <td>0.471075</td>\n",
              "      <td>0.391950</td>\n",
              "      <td>0.551300</td>\n",
              "      <td>0.503725</td>\n",
              "      <td>0.564875</td>\n",
              "      <td>0.517025</td>\n",
              "      <td>0.460500</td>\n",
              "      <td>0.642300</td>\n",
              "      <td>0.439400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.646900</td>\n",
              "      <td>0.467900</td>\n",
              "      <td>0.543400</td>\n",
              "      <td>0.477200</td>\n",
              "      <td>0.497400</td>\n",
              "      <td>0.846400</td>\n",
              "      <td>0.573875</td>\n",
              "      <td>0.501675</td>\n",
              "      <td>0.519825</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.412375</td>\n",
              "      <td>0.479400</td>\n",
              "      <td>0.646325</td>\n",
              "      <td>0.565200</td>\n",
              "      <td>0.610075</td>\n",
              "      <td>0.506975</td>\n",
              "      <td>0.671725</td>\n",
              "      <td>0.514175</td>\n",
              "      <td>0.524175</td>\n",
              "      <td>0.501325</td>\n",
              "      <td>0.516975</td>\n",
              "      <td>0.517475</td>\n",
              "      <td>0.375275</td>\n",
              "      <td>0.572250</td>\n",
              "      <td>0.251600</td>\n",
              "      <td>0.540950</td>\n",
              "      <td>0.530900</td>\n",
              "      <td>0.527900</td>\n",
              "      <td>0.475275</td>\n",
              "      <td>0.522075</td>\n",
              "      <td>0.648500</td>\n",
              "      <td>0.492150</td>\n",
              "      <td>0.522500</td>\n",
              "      <td>0.457000</td>\n",
              "      <td>0.483000</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>0.536950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.039000</td>\n",
              "      <td>8.257000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.282000</td>\n",
              "      <td>7.333000</td>\n",
              "      <td>5.473000</td>\n",
              "      <td>8.887000</td>\n",
              "      <td>6.433000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.134000</td>\n",
              "      <td>6.418000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>8.872000</td>\n",
              "      <td>4.081000</td>\n",
              "      <td>9.842000</td>\n",
              "      <td>5.248000</td>\n",
              "      <td>5.942000</td>\n",
              "      <td>5.201000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.494000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.416000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.796000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.834000</td>\n",
              "      <td>5.602000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.444000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.889000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.635000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.664000</td>\n",
              "      <td>9.566000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.497000</td>\n",
              "      <td>6.911000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.323000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.533000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.008000</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.552000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.346000</td>\n",
              "      <td>5.444000</td>\n",
              "      <td>6.317000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.911000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 772 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                g-0           g-1  ...         g-770         g-771\n",
              "count  23814.000000  23814.000000  ...  23814.000000  23814.000000\n",
              "mean       0.248366     -0.095684  ...     -0.219210      0.101524\n",
              "std        1.393399      0.812363  ...      1.326193      1.417674\n",
              "min       -5.513000     -5.737000  ...    -10.000000    -10.000000\n",
              "25%       -0.473075     -0.562200  ...     -0.554400     -0.523800\n",
              "50%       -0.008850     -0.046600  ...      0.028700     -0.006500\n",
              "75%        0.525700      0.403075  ...      0.496400      0.536950\n",
              "max       10.000000      5.039000  ...     10.000000     10.000000\n",
              "\n",
              "[8 rows x 772 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4B8eOBCtJZqR"
      },
      "source": [
        "###Histograma das médias, minimos e máximos das variáveis *g-0 .. g-771*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "p0zH98wRJMVU",
        "outputId": "17f18b8d-4a8b-4130-f7a9-769751e8f313"
      },
      "source": [
        "# .T faz a transposição da matriz (linhas em colunas)\n",
        "dados.loc[0:,'g-0':'g-771'].describe().T"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g-0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.00885</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>-5.737</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.04660</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>0.07520</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>8.257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>-5.998</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>0.00805</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.02690</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>10.000</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>g-767</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>0.00990</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>6.317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-768</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>-4.269</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>0.00540</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-769</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>0.00060</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>5.911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-770</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-771</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>1.417674</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.523800</td>\n",
              "      <td>-0.00650</td>\n",
              "      <td>0.536950</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>772 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean       std     min       25%      50%       75%     max\n",
              "g-0    23814.0  0.248366  1.393399  -5.513 -0.473075 -0.00885  0.525700  10.000\n",
              "g-1    23814.0 -0.095684  0.812363  -5.737 -0.562200 -0.04660  0.403075   5.039\n",
              "g-2    23814.0  0.152253  1.035731  -9.104 -0.437750  0.07520  0.663925   8.257\n",
              "g-3    23814.0  0.081971  0.950012  -5.998 -0.429575  0.00805  0.463400  10.000\n",
              "g-4    23814.0  0.057347  1.032091  -6.369 -0.470925 -0.02690  0.465375  10.000\n",
              "...        ...       ...       ...     ...       ...      ...       ...     ...\n",
              "g-767  23814.0 -0.076251  1.115477 -10.000 -0.506200  0.00990  0.511175   6.317\n",
              "g-768  23814.0  0.134162  0.951264  -4.269 -0.353100  0.00540  0.409075  10.000\n",
              "g-769  23814.0 -0.128018  1.230636 -10.000 -0.544600  0.00060  0.498500   5.911\n",
              "g-770  23814.0 -0.219210  1.326193 -10.000 -0.554400  0.02870  0.496400  10.000\n",
              "g-771  23814.0  0.101524  1.417674 -10.000 -0.523800 -0.00650  0.536950  10.000\n",
              "\n",
              "[772 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "wvTSl1baJmKs",
        "outputId": "709a417d-c338-477c-c041-f460234daf67"
      },
      "source": [
        "dados.loc[0:,'g-0':'g-771'].describe().T['mean'].hist(bins=30)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab0eea0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "OmDw1dPXKC0E",
        "outputId": "666ed07b-a2d5-4b95-c5ab-8a20b04ee1c9"
      },
      "source": [
        "dados.loc[0:,'g-0':'g-771'].describe().T['min'].hist(bins=30)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab09ac2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "w6Xug5tBKIWa",
        "outputId": "8e268b08-225b-47fa-9e12-59d80f6c33ef"
      },
      "source": [
        "dados.loc[0:,'g-0':'g-771'].describe().T['max'].hist(bins=30)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcaafffa710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KK73eW6sMtZN"
      },
      "source": [
        "###Avaliando a histograma das médias das variáveis *c-0 .. c-99*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "vP-hc1j_Ml9p",
        "outputId": "5cca1b65-15da-46f5-bb15-528832964cc1"
      },
      "source": [
        "# as variáveis c-0 .. c-99 representam linhagens celulares (tipos celulares)\n",
        "dados.loc[0:,'c-0':'c-99'].describe().T['mean'].hist(bins=50)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcaafac4f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD7CAYAAAB68m/qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATq0lEQVR4nO3de2xUZf7H8c9Ma1sK05SWaR0BRY0gugpJ2XRj1j+2VEFo6YLBapVNugvGGLwkXmBZVi41sOOFSFYJEKMxuwQ3JAppUesFAglegq6sIihut2hjC4UpKy1gCzNn/9gf/em2Mz0z58yFh/frL/r0zPN8v3NOPzk5nT54LMuyBAC44HnTXQAAwB0EOgAYgkAHAEMQ6ABgCAIdAAxBoAOAIQh0ADBEdroLOHHilCKR5H0Uvrh4hEKhnqTNnyr0kVnoI7OY0IfdHrxej0aOHD7o99Ie6JGIldRAP7+GCegjs9BHZjGhD6c98MgFAAxBoAOAIQh0ADAEgQ4AhiDQAcAQBDoAGIJABwBDpP1z6Eg9X8Ew5eUOPPU/9J5T98kzaagIgBsI9ItQXm62qh/ZNmC88dkadaehHgDu4JELABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCGINABwBAEOgAYgkAHAEPYCvRgMKiKigpNmDBBhw4dkiSdOHFCCxYs0LRp01RdXa2FCxeqq6srqcUCAKKzFehTp07Vpk2bNHr06P4xj8ej+fPnq7m5WY2NjRo7dqyeeeaZpBUKAIjNVqBPmTJFgUDgJ2OFhYUqLy/v/3ry5Mlqb293tzoAgG2uPEOPRCLavHmzKioq3JgOAJAAV7bPbWhoUH5+vu655564X1tcPMKNEmLy+31JXyMVUtGHKWukAn1kFhP6cNqD40APBoP65ptvtH79enm98d/wh0I9ikQsp2VE5ff7dOzYhb/Lt5t9xLpokv1ecT4yC31kDrs9eL2eqDfCjgJ9zZo12r9/vzZu3KicnBwnUwEAHLIV6E8++aTefvttHT9+XPX19SosLNRzzz2nDRs2aNy4cbrzzjslSWPGjNELL7yQ1IIBAIOzFehLly7V0qVLB4x/9dVXrhcEAEgMfykKAIYg0AHAEAQ6ABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCGINABwBAEOgAYwpX90JGZfAXDlJfLKQYuFvy0GywvN1vVj2wbMN74bE0aqgGQbDxyAQBDEOgAYAgCHQAMQaADgCEIdAAwBIEOAIYg0AHAEAQ6ABiCQAcAQxDoAGCIIQM9GAyqoqJCEyZM0KFDh/rHW1tbVVtbq2nTpqm2tlaHDx9OZp0AgCEMGehTp07Vpk2bNHr06J+ML1u2THV1dWpublZdXZ2eeOKJpBUJABjakIE+ZcoUBQKBn4yFQiEdOHBAVVVVkqSqqiodOHBAXV1dyakSADCkhJ6hd3R0qLS0VFlZWZKkrKwslZSUqKOjw9XiAAD2pX373OLiEUlfw+/3JX2NVEhFH6askQr0kVlM6MNpDwkFeiAQ0NGjRxUOh5WVlaVwOKzOzs4Bj2bsCIV6FIlYiZRhi9/v07Fj3UmbP1US6SORiyPZ79XFfD4yEX1kDrs9eL2eqDfCCT1yKS4u1sSJE9XU1CRJampq0sSJE1VUVJTIdAAAFwx5h/7kk0/q7bff1vHjx1VfX6/CwkJt375dy5cv1+LFi7Vu3ToVFBQoGAymol4AQBRDBvrSpUu1dOnSAeNXX321tmzZkpSiAADx4y9FAcAQBDoAGIJABwBDEOgAYAgCHQAMQaADgCEIdAAwBIEOAIYg0AHAEAQ6ABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCGINABwBAEOgAYgkAHAEMQ6ABgCAIdAAxBoAOAIQh0ADBEttMJdu7cqbVr18qyLFmWpYULF+rWW291ozYAQBwcBbplWXr88ce1adMmjR8/Xl9++aXuuusuVVZWyuvl5h8AUslx6nq9XnV3d0uSuru7VVJSQpgDQBo4ukP3eDx67rnndP/99ys/P1+nTp3Sxo0b3aoNABAHR4F+7tw5bdiwQevWrVNZWZk++eQTPfzww9q+fbuGDx9ua47i4hFOSrDF7/clfY1USEUfpqyRCvSRWUzow2kPjgL94MGD6uzsVFlZmSSprKxMw4YNU0tLi2688UZbc4RCPYpELCdlxOT3+3TsWHfS5k+VRPpI5OJI9nt1MZ+PTEQfmcNuD16vJ+qNsKOH3ZdeeqmOHDmif/3rX5KklpYWhUIhXX755U6mBQAkwNEdut/v1/Lly/XQQw/J4/FIklatWqXCwkJXigMA2Of4c+izZs3SrFmz3KgFAOAAny8EAEMQ6ABgCAIdAAxBoAOAIQh0ADAEgQ4AhiDQAcAQBDoAGIJABwBDEOgAYAgCHQAMQaADgCEIdAAwBIEOAIYg0AHAEAQ6ABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCGINABwBDZTifo7e3VqlWr9MEHHyg3N1eTJ09WQ0ODG7UBAOLgONCffvpp5ebmqrm5WR6PR8ePH3ejLgBAnBwF+qlTp7R161bt2rVLHo9HkjRq1ChXCgMAxMfRM/S2tjYVFhbq+eef15w5czRv3jx9/PHHbtUGAIiDozv0cDistrY2XXfddVq0aJH+8Y9/6L777tM777yjESNG2JqjuNjecU74/b6kr+GmvrNh5VySNWC8oDBfuYOM954NDzqeiFS8Vxfa+YiGPjKLCX047cFRoAcCAWVnZ6uqqkqSNGnSJI0cOVKtra264YYbbM0RCvUoErGclBGT3+/TsWPdSZs/Gfx+n6of2TZgvPHZmrjH45Xs9+pCPB+DoY/MYkIfdnvwej1Rb4QdPXIpKipSeXm59uzZI0lqbW1VKBTSFVdc4WRaAEACHH/KZcWKFVqyZImCwaCys7P11FNPqaCgwI3aAABxcBzoY8eO1V/+8hc3agEAOMBfigKAIQh0ADAEgQ4AhiDQAcAQBDoAGIJABwBDEOgAYAgCHQAMQaADgCEIdAAwBIEOAIYg0AHAEAQ6ABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCGINABwBAEOgAYgkAHAEMQ6ABgCNcC/fnnn9eECRN06NAht6YEAMTBlUD/4osvtG/fPo0ePdqN6QAACXAc6H19fVq5cqWWL1/uQjkAgEQ5DvS1a9dq1qxZGjNmjBv1AAASlO3kxZ9++qn279+vRx99NOE5iotHOCnBFr/fl/Q1EtF3NqycS7LSXUa/vrPhQd+r3rNh5Q5SZ7TxofoabI1or0nXeDQ/Pv7HfWTauYxHpv58xMuEPpz24CjQ9+7dq5aWFk2dOlWSdOTIEf3ud7/T6tWr9ctf/tLWHKFQjyIRy0kZMfn9Ph071p20+Z3w+32qfmTbgPHGZ2vSUI2Uc0lW1HriHY/2nkc7H7Hei3QcH41b82SKTP75iIcJfdjtwev1RL0RdhTo9957r+69997+rysqKrR+/XqNHz/eybQAgATwOXQAMISjO/T/tWPHDjenAwDEgTt0ADAEgQ4AhiDQAcAQBDoAGIJABwBDEOgAYAgCHQAMQaADgCEIdAAwBIEOAIYg0AHAEK7u5ZJKvoJhyssdWP4PvefUffJMGirCeVH3Ve8LSzJj32ogE12wgZ6Xmx11X+oLe1fkC1+8+6qf/x4AZ3jkAgCGINABwBAEOgAYgkAHAEMQ6ABgCAIdAAxBoAOAIQh0ADAEgQ4AhiDQAcAQjv70/8SJE3r88cf17bffKicnR1dccYVWrlypoqIit+oDANjk6A7d4/Fo/vz5am5uVmNjo8aOHatnnnnGrdoAAHFwFOiFhYUqLy/v/3ry5Mlqb293XBQAIH6u7bYYiUS0efNmVVRUxPW64uIRbpUgafCtW/1+n/rOhpVzSZara/3vutHmT/bapot3u91kHz+YqFsGnw0rd5Bzn0nXhCnbGZvQh9MeXAv0hoYG5efn65577onrdaFQjyIRK+71ojUea+vWY8eSt7Gu3++LuTXsYGubcAGmQrzvnRvHR5PIdZeO69Euv9+XEXU4ZUIfdnvwej1Rb4RdCfRgMKhvvvlG69evl9fLB2cAIB0cB/qaNWu0f/9+bdy4UTk5OW7UBABIgKNA//rrr7VhwwaNGzdOd955pyRpzJgxeuGFF1wpDgBgn6NAv+aaa/TVV1+5VQsAwAEeeAOAIQh0ADAEgQ4AhiDQAcAQBDoAGIJABwBDEOgAYAgCHQAMQaADgCEIdAAwhGvb516ofAXDlJc78G34ofecuk+esX08kiPaPuPJPr63L6zcnOTunx+tzmjXXrzivbbhvlSfg4s+mfJys6PuVz3YzsSxjof7Yu0znuzjk3meo617fg03dvaO99qG+1J9DnjkAgCGINABwBAEOgAYgkAHAEMQ6ABgCAIdAAxBoAOAIQh0ADAEgQ4AhiDQAcAQBDoAGMJxoLe2tqq2tlbTpk1TbW2tDh8+7EJZAIB4OQ70ZcuWqa6uTs3Nzaqrq9MTTzzhRl0AgDg52m0xFArpwIEDevnllyVJVVVVamhoUFdXl4qKimzN4fV6El6/ZOSwuMajreXW8dHG3ZzL1PFMrCmd74WTnws7a/x4frfWSrdM7SOefLHTQ6xjPJZlWfZL+6n9+/dr0aJF2r59e//YjBkz9PTTT+v6669PdFoAQAL4pSgAGMJRoAcCAR09elThcFiSFA6H1dnZqUAg4EpxAAD7HAV6cXGxJk6cqKamJklSU1OTJk6caPv5OQDAPY6eoUtSS0uLFi9erJMnT6qgoEDBYFBXXXWVW/UBAGxyHOgAgMzAL0UBwBAEOgAYgkAHAEMQ6ABgCOMC/cyZM3r44Yd1yy23aPr06dq5c2fUYw8ePKi7775bM2bM0IwZM7Rr164UVhqb3T4++ugjTZo0STU1NaqpqdHcuXNTXGls8ZwPSert7dXMmTM1Z86cFFU4NLs9HDx4ULNnz1ZNTY1mzpypP/7xj+rr60txtdHZ7ePdd9/VnDlzVFVVpZkzZ+qll15KcaWx2e3j6NGjmjdvnsrKyjLqerKzoWE4HNaKFStUWVmpW265RVu2bLE3uWWYP//5z9Yf/vAHy7Isq7W11brpppusnp6eAcedOnXKqqiosD799FPLsizr7NmzVldXV0prjcVuHx9++KE1e/bsVJdnm90+zlu9erX1+9//PqN6stvDmTNnrN7eXsuyLCscDlsLFy60XnnllZTWGovdPvbt22cdOXLEsizLOnnypFVZWWnt3bs3pbXGYrePkydPWnv37rV27tyZUdfTvHnzrK1bt1qWZVlbt2615s2bN+CY119/3frtb39rhcNhKxQKWTfffLPV1tY25NzG3aG/+eabqq2tlSSNGzdOP/vZz7R79+4BxzU1NamsrEyTJ0+WJGVnZ2vkyJEprTUWu31kunj6+Pjjj3X48GHV1NSkssQh2e0hLy9POTk5kqRz587phx9+kNebOT9idvuYNGmSSktLJUk+n09XX321vvvuu5TWGovdPnw+n6ZMmaJhw6JvgpZq5zc0rKqqkvTfDQ0PHDigrq6unxz3xhtvaO7cufJ6vSoqKlJlZaXeeuutIefPnKvNJe3t7Ro9enT/14FAQEeOHBlw3D//+U9lZ2drwYIFqqmp0ZIlS/T999+nstSY7PYhSYcPH9bs2bM1d+5cvf7666kq0Ra7fZw+fVqrVq3SihUrUlmeLfGci6NHj6qmpkbl5eUaPny47rjjjlSVOaR4+jivpaVF+/bt0y9+8Ytkl2dbIn1kio6ODpWWliorK0uSlJWVpZKSEnV0dAw47rLLLuv/2m6PjrbPTYfZs2ervb190O+9//77tueJRCL68MMP9eqrr2rUqFFavXq1/vSnP2n16tVulRqTW31cf/312rVrl3w+n9ra2lRfX6/S0lLddNNNbpUak1t9PPXUU6qrq1NpaWnK/5MUt3qQpNLSUm3btk2nT5/WY489pnfeeUczZ850o8whudmHJHV2dur+++/XsmXL+u/YU8HtPi4mF1ygD3UHetlll+m7777r30+mo6ND5eXlA44LBAIqLy9XSUmJJKm6ulpLlixxv+Ao3OpjxIgR/f8eO3asKisr9fe//z1lge5WH5988ol2796tdevWqbe3V99//72qq6vV2NiYlLp/zK0efiw/P18zZsxQY2NjygLdzT5CoZDq6+s1f/583Xbbba7XGksyzkem+PGGhllZWVE3NAwEAmpvb9eNN94oaeAdezTGPXKZPn26/va3v0n676OIzz//XDfffPOA42677TZ99tln6unpkSTt3r1bEyZMSGmtsdjto7OzU9b/7d7w73//W3v27NG1116b0lpjsdtHY2OjduzYoR07dmjNmjUaP358SsLcDrs9tLW19X+qpa+vT++9957Gjx+f0lpjsdvHiRMnVF9fr7vvvjvjPjUl2e8jE9nd0HD69OnasmWLIpGIurq69O6772ratGlDzm/cXi6nT5/W4sWLdfDgQXm9Xj322GOqrKyUJK1du1YlJSW66667JElbt27Viy++KI/HozFjxqihoUGjRo1KZ/n97Pbx17/+VZs3b1Z2drbC4bB+/etfa/78+Wmu/v/Fcz7O++ijjxQMBvXaa6+lo+QB7Pawbdu2/uspEono5z//uRYtWqS8vLw0d/BfdvsIBoPatGmTrrzyyv7X/uY3v9Htt9+ertJ/wm4f4XBYv/rVr9TX16eenh4VFRVp7ty5euCBB9Jaf7QNDRcsWKAHH3xQN9xwg8LhsFauXKk9e/ZIkhYsWND/i+BYjAt0ALhYGffIBQAuVgQ6ABiCQAcAQxDoAGAIAh0ADEGgA4AhCHQAMASBDgCG+A+dnAYA/AActgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8aY9RS-OgqP"
      },
      "source": [
        "###Boxplot da expressão genética do gene *g-0*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "PJQguPtfOjAE",
        "outputId": "6921a3bd-1f42-4088-d497-265016e2e1cb"
      },
      "source": [
        "sns.boxplot(x='g-0', data=dados)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcab0000490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "U9j9IATBQtAo",
        "outputId": "691ef948-d760-4770-e836-5829d8b03294"
      },
      "source": [
        "plt.figure(figsize=[10, 8])\n",
        "ax = sns.boxplot(y='g-0', x='tratamento', data=dados)\n",
        "ax.set_title('Avaliação do tratamento', fontsize=25)\n",
        "ax.set(xlabel='Tipo de tratamento', ylabel='Expressão genética')\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH/CAYAAAASb3qiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZyNdeP/8fc5M8MwgxkTEu60ONbozpa4qRSV/b6J3Lm1p+WLbt8QhVSWFiJKVEiKbDFUclu6sy8pJJQlYwuzMGYY5pzP7w/fOT/HLM6YOXNdM+f1fDx65HOd5XqPuZx5z+faHMYYIwAAANiC0+oAAAAA+P8oZwAAADZCOQMAALARyhkAAICNUM4AAABshHIGAABgI5QzwCLVq1dX9erVtWHDhlw9ZpUffvhBrVq1Ur169dS9e3ctXbpUqamp+uc//6m///3vVsfL1nvvvafq1aurR48eVkcBAL+EWh0AyG+nTp3S3/72N6WlpUmSli5dqqpVq1obqpAzxqh///5KSkpSiRIltGXLFm3ZssX7eL9+/SxMZ08bNmzQxo0bValSJcvL6+nTpzV9+nRJUs+ePVW6dGlL8xRGdvp+ouijnKHIiY2N9RYzSZo3b16hKw833HCDJKlEiRIWJ7koNTVVHTt2VNOmTdW0aVPt3LlTGzZs0KlTp1S7dm21atXK6oi2s3HjRk2YMEGNGjWy/If56dOnNWHCBElSp06dKGdXwU7fTxR9lDMUOXPnzpUk9ejRQzNmzNCCBQvUt29fhYSEWJzMf99++63VEXxERERowIAB3nHt2rVVu3ZtCxMBQNHFMWcoUn755Rf9+uuvKl26tF588UVVrlxZJ06c0Pfff291NAAA/MLMGYqUjFmz+++/X8WLF1fHjh01YcIEzZs3T3fffbfPc6dNm6aRI0cqJiZG//3vfxUamvU/B2OMWrZsqcOHD6t379567rnnJEkej0cbNmzQ8uXLtW3bNh07dkwJCQmKiIhQtWrV1KZNG3Xu3FlhYWG5/jqqV68uSfr000/VuHFjn8f27NmjpUuXatOmTTpy5IiOHz+u0NBQ/eUvf1GLFi3Us2dPlS1bNsf337t3rz777DNt2LBBR48elSSVL19etWrV0n333ad7771XTuf//93tt99+07fffptpnVWqVFGLFi30yCOPXHGd3333nebNm6ft27fr9OnTKl26tG655RZ17txZ9957b67/ji71/fffa9q0adq+fbvcbreqVKmidu3a6ZFHHvHr9Rs2bNDMmTO1detWJSYmKiIiQjVq1FD79u3VsWPHXM26Hjp0SC1btvSON27c6P1+Zhg5cqR311iPHj20ceNGPf/88+rVq5dmzJihxYsX6+DBg0pOTvZuA1e7vWW8f4ZLs0lSo0aNNGPGDEl526Yv/bqXL18uh8OhDz74QKtXr1Z8fLwqVKigNm3a6Omnn1bJkiUlXdyWJ0+erE2bNikhIUEVK1ZUx44d9eSTT+b47+bQoUOaPn261q5dqyNHjsjj8ahixYpq1qyZHnvsMV133XWZXjN//ny99NJLqlSpklasWKEdO3ZoypQp2rJli5KSklShQgXdc889evbZZ1WmTJmr/n5myM9tCsGHcoYiIy0tTYsXL5YkdezY0fv/iRMnatWqVTp58qSuueYa7/Pbtm2rN998U/Hx8Vq9erXuvPPOLN9306ZNOnz4sBwOhzp06OBdfuTIEZ8f/iVLllR4eLiSkpK0adMmbdq0SYsXL9bHH3+s8PDwfPs6e/XqpcOHD0uSihcvrhIlSujUqVP69ddf9euvv2rBggWaNm2abrzxxixfP3nyZI0dO1Yej8fnPQ4ePKgDBw7o66+/1qZNm3yOS3r22Wd18ODBTOvctWuXdu3apfnz52vatGm6+eabM63v/PnzGjBggL7++mtJktPpVKlSpZSYmKhVq1Zp1apVatu2rUaNGnVVRfa9997zHk8lSaVLl9bevXv19ttv6/vvv9dtt92W4+tHjhypadOmSZIcDodKlSql5ORkrV+/XuvXr9eiRYs0ceJERUZG+pUnJCRE11xzjVJTU5WamqqwsDCfH/aSstwe0tLS1KNHD23dulWhoaGKiIiQw+HwPn6121uZMmUUHR2txMRESVJ0dLRPMbg0W35t0zt37tTgwYN1+vRpRUZGyu12Ky4uTpMmTdLmzZs1bdo0rVmzRn379tXZs2dVqlQpXbhwQX/88YfGjRun3377TWPHjs3yvRctWqTBgwfr/PnzkqRixYrJ6XRq//792r9/v+bPn6/x48erWbNm2eaLjY3VSy+9pAsXLqhUqVJyu906dOiQN9fs2bMVEREh6eq+n/m9TSEIGaCIWLhwoXG5XObee+/1Wd69e3fjcrnMlClTMr3miSeeMC6Xy/Tt2zfb9x00aJBxuVzmn//8p8/yo0ePmn79+pnly5ebxMRE7/IzZ86YefPmmWbNmhmXy2VGjBiR5fu6XC7jcrnM+vXrc/VY//79zfz5883hw4e9y9LS0szatWtN586djcvlMp06dcpynTNnzvS+d69evczOnTu9j6WmpprVq1ebvn37muTkZJ/XDRw40MybNy/HdXbo0CHLdY4aNcq4XC5TvXp18+6775pTp04ZY4xJSkoyY8aM8eZ56623snx9Tv7zn/94X9+7d29z5MgRY4wxZ8+eNZ999pmpXbu2adCggXG5XObhhx/O9PoZM2Z4X//KK6+Y48ePG2OMSUlJMVOnTjW1atW64vaRnfHjx2e73ks9/PDDxuVymVtvvdXceuutZt68eebs2bPGGGMSEhK821Zetre4uDjv1xkXF5dtlvxaR4MGDUzPnj3Nb7/9Zoy5+P349NNPTc2aNY3L5TJjx4419evXN3379jWHDh3yruPS7WHNmjWZ1rF69WpTo0YNU6tWLfPmm2+auLg44/F4jMfjMXv37jW9e/c2LpfL3HbbbT7bqjHGzJs3z7hcLlOvXj1Tp04dM3jwYO/2kpqa6t1eXC6XeffddzOt29/vZyC3KQQPyhmKjB49ehiXy2UmTJjgs/zLL780LpfL3HfffZles3jxYuNyucwtt9ySqZAYY8y5c+dM/fr1jcvlMl9++WWu8mzbts37Q/fcuXOZHr/acpaTM2fOmDvuuMO4XC6zadMmn8eSkpLMX//6V+NyucwLL7xgPB5Prt77atZ57Ngx7w+jd955J8vXjxw50rhcLlO7dm3z559/5mrdDzzwgPcHptvtzvT4F1984f27vPyH6tmzZ02jRo2My+Uy//73v7N8/08//dT7+u3bt+cqW27LmcvlMsuXL8/VOi6V0/bmbznLr3W0adPGpKWlZXr9iy++6H3Oo48+muU2mPHL1KBBg3yWu91u06pVK+NyucysWbOyzdirVy/jcrnM66+/7rM8o5y5XC4zYMCALF+bsS1e/gueMf59PwO9TSF4cEIAioS4uDht3Lgx065H6eLxZ+Hh4dq3b59+/PFHn8datmypyMhIpaWl6Ztvvsn0vsuXL1dycrKKFy+u++67L1eZbrnlFsXExCg1NVW//vpr7r+oqxAREaGGDRtKUqavdenSpUpJSVFYWJgGDhzos8ssv9Z56bXPMtaZnp6u4sWL66mnnsry9c8884yKFSumCxcuaOnSpX6vd9euXfr999+973HpMXIZHnzwQVWoUCHL169Zs0ZJSUmSpOeffz7L53Tv3l3lypWTJO8u80CpVq1apuMic6Mgtjd/19GzZ08VK1Ys0/JLdzU++eSTWW6DGc/ZvXu3z/JNmzbpwIEDio6OVpcuXbJdd8YhDatXr872Oc8880yWyzOOLfvjjz909uzZbF+fHbttUyi8OOYMRcK8efNkjFHDhg1VuXJln8ciIyN1zz33aPHixZo7d67PMUjh4eFq3bq15s2bp0WLFmX60F+0aJGkix/apUqVyrTe8+fPa968eVq2bJn27NmjpKQkXbhwIdPzjh07lh9fptfKlSu1cOFCbd++XfHx8Vn+ILl8nVu3bpV08TIY5cuXD8g6//zzT5/xjh07JF38oZ7d8TVlypRRnTp19OOPP3qf74+M54aGhqpBgwZZPsfpdKpRo0aKjY3N9vUVK1b0XlfuciEhIbr99tsVGxubq2xX40rHxkkFs73lxzrq1q2b5fJLj/m85ZZbcnzO6dOnfZZn/LJx5swZ/e1vf8t23RlZjxw5kuXjUVFRuv7667N87NJ/F6dPn871dQbttk2h8KKcodDzeDxasGCBJGWaNcvQsWNHLV68WN98840GDx7sPdg347F58+Z5D/yvVKmSJCkhIUE//PCD9zmXi4+P1yOPPKI9e/Z4lxUvXtzngOuEhAR5PJ6r+i08u6/1xRdf9PmNOzQ0VGXKlPEeTJ+cnKy0tLRM6zxx4oQkZXkmW6DWGR8fL0nZzl5luPbaa32e74+EhARJFw9wz2qW5vL3vlwgs12NK53tWhDbW36t49J/X5e69ESE7Mp6xnPS09N9lh8/flzSxfJ18uTJK34t586dy1W2y/NlVUivxG7bFAovyhkKvR9++MH7W/zLL7+sl19+Odvnpqam6ptvvlHnzp29yxo2bKhKlSrp8OHDWrRokXeXx5IlS5Senq5rrrlGTZs2zfReI0aM0J49exQVFaX+/furefPm3t0VGVq0aKFjx47JGJMfX6rmzp2rxYsXKyQkRL169VKHDh1UpUoVn116L774ohYtWpRpnVe7GzMv64T/rnRphYLY3qzYpv3ldrslSfXq1dOXX35ZoOsGChrHnKHQmzdvXq6en3EttAwOh0Pt2rWTJC1cuNC7POPPbdq0yXQNtAsXLmjZsmWSpCFDhugf//hHph9ibrfbe/mC/LJkyRJJUufOndW7d29df/31mY61ym5WIWN3UXa7ewKxzpiYGElX3s2W8XjG8/2RMdOUmJjovaxCVi7f1VoQ2fJbQWxvVm3T/srIktvttyAVpm0K9kY5Q6GWkJCgFStWSJLGjx+vH3/8Mdv/5syZI+nisVf79u3zeZ+M3Zb79+/Xtm3btG/fPm3fvt3nscvXm3H/zpo1a2aZbcuWLT73+MwPGR/qtWrVyvLxlJQU/fzzz1k+lnFM044dO7y7iAK9zjp16njXmZycnOVzTp8+7XNsmr8y3js9PT3TiQgZPB6PzwVYs3r9sWPHtH///iyf43a7tWHDhlxnk/7/TGV+zDDldXu7tExnl8eqbdpfGdvviRMnvP82C5I/389Ab1MIHpQzFGoLFy70XkjyrrvuUkRERLb/1a1b13th1stnz2644QbVq1fP+54Zs2YulyvLUhIZGen9sN61a1emx9PT07O9iGZeZBynk9U6Jen9999XSkpKlo/dd999ioyMVHp6ukaOHOl3acjLOlu3bq3Q0FClpaVpypQpWT5n0qRJOn/+vMLCwnJ1A/UaNWropptukiR98MEH3ovqXmrevHnZzmI0bdpUUVFRkuRzEdtLzZo1y1tk27Rp43c26f//vV1+YPvVyOv2dunxXdmVZKu2aX81btzYeyD/yJEjc5wtleQ9azK/+PP9DPQ2heBBOUOhllGyWrZsmeNB4RkyLoexcOHCTAccZ5xMsGTJEu/Zfe3bt8/yfSIiIry/yY8aNUrr1q3zloM9e/boqaee0o4dO7y3qckvGWepzZkzR7Nnz/b+gDpx4oRGjBihjz76yPvD4XKlSpXSiy++KEn6+uuv9dxzz/lcDuHs2bNatWqVnnnmGZ05cyZf1lmhQgX961//knTxzgTjx4/3/nA7ffq03n33XX388ceSpEceeSTXZ5G+8MILki7eKqdfv37eIpaWlqYvvvhCw4cP97nTwaXCw8P1P//zP5IuXtJgyJAh3t2zZ8+e1aeffqqRI0dKkh544AHvrIi/XC6XJOn333/PdFmT3Mrr9la6dGnvQerz58/PtO3nxzoCLTQ0VK+++qpCQ0O1ZcsWPfzww1q3bp3PgftxcXH64osv9I9//EOff/55vq7fn+9noLcpBA9OCECh9dNPP3mvc+XvNcjuu+8+vf/++zp58qRWrVqle+65x/vYAw88oJEjRyoxMVGJiYlyOp3ZljNJGjRokHr06KE///xTjzzyiIoVK6awsDClpKQoNDRUb7zxhsaPH6/U1NS8faGXeOyxx7R06VLt27dPQ4YM0bBhwxQZGank5GQZY9S1a1edP3/ee/bq5bp166ZTp07p3Xff1fLly7V8+XKFh4crPDxcp0+f9v4wvnQWKq/rfOGFF3T06FF98803mjhxoj744APv7Wwy1tO2bVv16dMn138f9957r3r16qVJkybp66+/1tdff60yZcooJSVF6enpatCggerXr68PP/wwy9c//PDDiouL07Rp0zR79mx9+eWXKl26tPf10sUZm9deey3X2Ro1aqQbbrhB+/fv10MPPaQyZcp4Z1/69++f6+vm5XV769atm8aNG6cZM2Zo9uzZiomJkdPpVL169bwzYlZs07nRpEkTjRs3Tv3799fPP/+sRx55RGFhYYqIiFBqaqrPbNql/7bzg7/fz0BuUwgezJyh0MqYNStVqlSWZ1NmpXr16t5dYZfv2oyOjlaLFi284yZNmuR4SnydOnU0Z84c3X///YqOjpYxRhEREbr//vv1xRdfZHmsWl6VLl1as2bNUs+ePVWpUiWFhIQoJCREjRo10pgxYzR8+PArvsfTTz+thQsX6sEHH9T111+vc+fOKSkpSZUrV1bbtm31/vvv+1zTLa/rLFasmN59912NHz9ezZs3V1RUlFJSUhQVFaXmzZtrwoQJeuedd67qvprSxfL34Ycf6vbbb1dkZKTOnz+vG2+8Uf369dO0adOu+L4vvfSSpk+frtatW3vvoRgREaHGjRtrxIgRmjp16lXdAzE0NFTTp09Xly5dVLlyZaWmpurw4cM6fPjwVZWbvG5vvXr10uDBg1WnTh2Fhobq2LFjOnz4sM/JHFZs07l1zz33aNmyZXr++edVt25dlSxZUsnJySpWrJhq1KihLl26aOLEiXr88cfzdb25+X4GaptC8HAYzn0HglpCQoJatWqlnj17enfJAACsw8wZEOTKli2rNm3a6OOPP/Y51gwAYA3KGRCk4uLiJF28vtWRI0d09uzZTJcYAQAUPE4IAILUM888o8OHD8sYo7Nnz6pYsWKqUqWK1bEAIOgxcwYEqXr16qlYsWJyu92qVq2a3nnnHUVHR1sdCwCCHicEAAAA2AgzZwAAADZSpI45S0xMkcfDRCAAALAvp9Oh6OiIbB8vUuXM4zGUMwAAUKixWxMAAMBGKGcAAAA2QjkDAACwEcoZAACAjVDOAAAAbIRyBgAAYCOUMwAAABuhnAEAANgI5QwAAMBGKGcAAAA2QjkDAACwEcoZAACAjRSpG58DVzJ8+BAdOPC7br7ZpUGDhlkdBwCATAp85mz06NG6++67Vb16de3Zs8e7fP/+/eratatat26trl276sCBAwUdDUHgwIHfJUm//77nCs8EAMAaBV7OWrZsqZkzZ6pSpUo+y4cOHaru3btr6dKl6t69u4YMGVLQ0VDEDR/uu02NGDHMmiAAAOSgwMtZgwYNVLFiRZ9l8fHx2rlzp9q2bStJatu2rXbu3KmEhISCjociLGPWLAOzZwAAO7LFMWdHjx5VhQoVFBISIkkKCQlR+fLldfToUZUtW9bv94mJiQxURBRR5cqVsjoCAAA+bFHO8kt8/Bl5PMbqGChETpxItjoCACDIOJ2OHCeUbHEpjYoVK+rPP/+U2+2WJLndbh0/fjzT7k8gL0JDfX8XCQsLsygJAADZs0U5i4mJUc2aNbV48WJJ0uLFi1WzZs1c7dIEriQkxLecOZ0hFiUBACB7BV7OXn/9dTVv3lzHjh3To48+qjZt2kiShg0bps8++0ytW7fWZ599pldffbWgo6GIq1+/oc+4QYNGFiUBACB7DmNMkTlIi2POkJOPPvpAa9f+4B3fccff9MQTz1iYCAAQjArFMWdAQdiyZVOOYwAA7IByhqARFRWd4xgAADugnCFonDjxZ45jAADsgHIGAABgI5QzBI1y5Sr4jMuXr5DNMwEAsA7lDEEjKSnRZ5yYmJjNMwEAsA7lDEGD65wBAAoDyhmCxvnz53McAwBgB5QzBI2tWzf7jH/8keucAQDsh3KGoHH5vTCKzr0xAABFCeUMQaNs2RifcUxMTDbPBADAOpQzBI2EhHifcXx8fDbPBADAOpQzBA2Px53jGAAAO6CcAQAA2AjlDAAAwEYoZwgaxYsXz3EMAIAdUM4QNNLS0nIcAwBgB5QzAAAAG6GcAQAA2AjlDAAAwEYoZwAAADZCOUPQcDpDchwDAGAHlDMEDe4QAAAoDChnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcoYg5rA6AAAAmVDOEMSM1QEAAMiEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANhJqdYBLrVy5UuPGjZMxRsYYPf/882rVqpXVsQAAAAqMbcqZMUb9+/fXzJkz5XK5tGvXLj300EO655575HQywQcAAIKDrVqP0+lUcnKyJCk5OVnly5enmAEAgKBim5kzh8Ohd999V88++6xKliyplJQUTZ48OVfvERMTGaB0KKrKlStldQQAAHzYppylp6frww8/1Pvvv6/69etry5Yt6tu3r5YsWaKIiAi/3iM+/ow8Hq76Dv+dOJFsdQQAQJBxOh05TijZZp/hr7/+quPHj6t+/fqSpPr166tEiRLau3evxckAAAAKjm3K2bXXXqtjx45p3759kqS9e/cqPj5ef/nLXyxOBgAAUHBss1uzXLlyGjZsmPr06SOHwyFJGjFihKKioixOBgAAUHBsU84kqX379mrfvr3VMQAAACxjm92aAAAAoJwBAADYCuUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYSKjVAS6VlpamESNGaN26dSpevLhuvfVWvfbaa1bHAgAAKDC2KmdvvfWWihcvrqVLl8rhcOjkyZNWRwIAAChQtilnKSkp+uqrr/T999/L4XBIkq655hqLUwEAABQs25SzuLg4RUVFacKECdqwYYMiIiLUp08fNWjQwO/3iImJDGBCFEXlypWyOgIAAD5sU87cbrfi4uJUq1YtDRgwQD///LN69eqlZcuWKTLSv9IVH39GHo8JcFIUJSdOJFsdAQAQZJxOR44TSrY5W7NixYoKDQ1V27ZtJUn16tVTdHS09u/fb3EyAACAgmObcla2bFk1btxYa9askSTt379f8fHxuv766y1OBgAAUHBss1tTkl599VUNGjRIo0ePVmhoqN58802VLl3a6lgAAAAFxlblrEqVKpoxY4bVMQAAACxjm92aAAAAoJwBAADYCuUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAPIoKSlRo0YN16lTSVZHQRFAOQMAII9iYxfot992a9Gi+VZHQRFAOQMAIA+SkhK1evX3MsZo9er/MnuGPKOcAQCQB7GxC+TxGEmSx+Nh9gx5RjkDACAP1q1bI7c7XZLkdqdr3bo1FidCYUc5AwAgD5o0aaqQkFBJUkhIqJo0aWpxIhR2lDMAAPKgXbtOcjodkiSn06n27f9ucSIUdpQzAADyICoqWs2atZDD4VCzZs1VpkyU1ZFQyIVaHQAAgMKuXbtOOnz4ELNmyBfMnAEAANiIXzNn6enp+vzzz7Vp0yYlJibKGON9bObMmQELBwBAYXDpRWh79HjM6jgo5PyaORs5cqRmz56tBg0a6JdfflGrVq0UHx+v22+/PdD5AACwNS5Ci/zmVzn77rvvNGXKFPXs2VMhISHq2bOnJk6cqA0bNgQ6HwAAtsZFaJHf/Cpn586dU8WKFSVJ4eHhOnv2rG666Sbt3LkzoOEAALA7LkKL/OZXObvpppu0fft2SVKdOnX03nvv6f3331eFChUCGg4AALvjIrTIb36Vs0GDBikkJESSNHDgQO3cuVMrV67Ua6+9FtBwAADYHRehRX7z62zNunXrev9ctWpVTZs2LVB5AAAoVDIuQrtq1XIuQot84dfM2eTJk7Vt2zafZdu2bdOUKVMCEgoAgMKkXbtOqlatOrNmyBd+lbNPP/1UN998s8+ym266SdOnTw9IqAkTJqh69eras2dPQN4fAID8FBUVrYEDhzBrhnzhVzm7cOGCQkN994CGhYXp/Pnz+R7ol19+0U8//aRKlSrl+3sDAADYnV/lrHbt2vr88899ls2aNUu1atXK1zDnz5/X8OHDNWzYsHx9XwAAgMLCrxMCXnrpJT366KNatGiRqlSpori4OJ04cUJTp07N1zDjxo1T+/btVbly5at6fUxMZL7mQdFXrlwpqyMAAODDr3JWrVo1LV26VKtWrdLRo0fVqlUr3XnnnYqIiMi3IFu3btWOHTv0v//7v1f9HvHxZ7xXaQb8ceJEstURAABBxul05Dih5Fc5k6SIiAi1adMmX0JlZdOmTdq7d69atmwpSTp27Jgef/xxjRw5Us2aNQvYegEAAOwk23L2+OOP6+OPP5Ykde/eXQ6HI8vnzZw5M1+CPPXUU3rqqae845b0NaEAACAASURBVLvvvluTJk2Sy+XKl/cHAAAoDLItZx07dvT+uUuXLgUSBgCAwigpKVGTJr2nZ57pzeU0kGfZlrN27dp5/3zjjTeqXr16mZ5z+YVp89OKFSsC9t4AAOSn2NgF+u233Vq0aL569HjM6jgo5Py6lMajjz6a5fInnngiX8MAAFDYJCUl6ocfvpcxRqtXf69Tp5KsjoRCLsdy5vF45Ha7ZYyRMUYej8f734EDB7w3QwcAIFjFxi6Q250uSUpPT9eiRfMtToTCLsezNWvVquU9EeDyC846nU716tUrcMkAACgE1q5dLWMuXsbJGKO1a1ezaxN5kmM5W758uYwx6tGjhz777DMZY+RwOORwOFS2bFmFh4cXVE4AAGwpJiZGR44c9hkDeZFjOcu4v+XKlSslXdzNefLkSZUvXz7wyQAAKATi4+NzHAO55dcJAadPn1a/fv1Ut25dtWrVStLFWbWxY8cGNBwAAHZ3xx3NchwDuZVtORswYID3z0OHDlVkZKRWrFihsLAwSdJf//pXffPNN4FPCACAjbVocbfP+M47W1qUBEVFtuWsWrVq6tevnyRp7dq1evnll1W+fHnvCQJly5Zl6hYAEPS+++6bHMdAbmVbzp544gl16NBBklS6dGklJib6PH7kyBGVK1cusOkAALC5DRvW+ozXr19jURIUFTkec9a8eXNJF2/f1Lt3b61fv14ej0dbt27VgAED1K1btwIJCQAAECz8OiHgySef1P3336/hw4crPT1dgwYNUsuWLdWzZ89A5wMAwNYaN74jxzGQWzleSiODw+FQz549KWMAAFymc+duWrv2B++4S5eHLEyDosCvciZJ+/bt065du5SamuqzvHPnzvkeCgCAwsThcHgv1A7klV/lbNKkSZo4caJq1Kjhc1cAh8NBOQMABLXY2AVyOp1yu91yOp1atGg+t29CnvhVzqZPn645c+aoRo0agc4DAEChsm7dGrndbkmS2+3WunVrKGfIE79OCAgPD9eNN94Y6CwAABQ6TZo0VUjIxbmOkJBQNWnS1OJEKOz8Kmd9+vTR66+/ruPHj8vj8fj8BwBAMGvXrpOczovHmjmdTrVv/3eLE6Gw82u35sCBAyVJc+bM8S7LOPDx119/DUwyAAAKgaioaDVr1kKrVi1Xs2bNVaZMlNWRUMj5Vc6WL18e6BwAABRa7dp10uHDh5g1Q77wq5xVqlRJkuTxeHTy5EmVL18+oKEAAChMoqKiNXDgEKtjoIjw65iz06dPq1+/fqpbt65atWol6eJs2tixYwMaDgAAINj4Vc6GDh2qyMhIrVixQmFhYZKkv/71r/rmm28CGg4AACDY+LVbc926dfrhhx8UFhbmvfpx2bJlFR8fH9BwAAAAwcavmbNSpUopMTHRZ9mRI0dUrly5gIQCAAAIVn6Vsy5duqh3795av369PB6Ptm7dqgEDBqhbt26BzgcAABBU/Nqt+eSTT6p48eIaPny40tPTNWjQIHXt2lU9e/YMdD4AAICg4lc5czgc6tmzJ2UMAIAs/PLLNo0ZM1r9+r2kWrXqWB0HhZzfJwRkpVixYrr22mu910EDACAYTZz4rowxmjBhjN5//xOr46CQ86ucDR48WMePH5ckRUVFKSkpSZIUExOjkydPqnr16hozZoyqVq0asKAAANjRL79s07lz5yRJ586d086dO5g9Q574dUJA586d1aNHD23evFmrV6/W5s2b1bNnT3Xr1k2bNm1SnTp19OqrrwY6KwAAtjNx4rs+4wkTxliUBEWFX+Xs008/Vb9+/RQeHi5JCg8PV9++fTV9+nSVLFlSAwcO1I4dOwIaFAAAO8qYNctuDOSWX+WsZMmS2r59u8+yX375RSVKlLj4Jk6/3gYAAABX4NcxZ71799Zjjz2mu+++WxUrVtSxY8e0cuVKvfLKK5IunjDQunXrgAYFAMCOoqLKKikpwTuOji5rYRoUBX6Vs44dO6pOnTpaunSpjh8/rqpVq2r27Nm6+eabJUl33XWX7rrrroAGBQDAjm6+uZo2b95wydhlYRoUBX6VM0m6+eabvWUMAABctG3bTz7jn3/ealESFBUcLAYAQB6EhITkOAZyi3IGAEAenD2bmuMYyC3KGQAAgI3kqpwdOXJEW7du1ZEjRwKVBwCAQqVs2RifcUxMTDbPBPzj1wkBx48f17///W/99NNP3ts31atXT2PGjFGFChUCnREAANvKuKVhhsTEpGyeCfjHr5mzYcOGqUaNGtq4caNWr16tjRs3qmbNmho6dGig8wEAYGsejzvHMZBbfs2cbdmyRePGjVNYWJiki3cM6N+/v/72t78FNBwAAECw8auclSlTRnv37lWNGjW8y/bt26fSpUvnW5DExET1799fBw8eVLFixXT99ddr+PDhKluWKy0DAIDg4Vc5e+KJJ/TII4+oc+fOuu6663TkyBHNnz9fffr0ybcgDodDTzzxhBo3bixJGj16tN5++22NGDEi39YB/6xZ81+tXv291TEKxOjRr1kdId81a9ZCTZs2tzoGEDScTqc8Ho/PGMgLv8rZgw8+qCpVqmjx4sXavXu3ypcvr3feeUdNmjTJtyBRUVHeYiZJt956q7744ot8e38AAALh0mKW1RjILYcxxlgd4nIej8d7o/V//etfVsdBEdGuXbtMy2JjYy1IAgSnFStWaNmyZVbHyHc7duzItKxOnToWJAmse++9V3fffbfVMYKC3/fW/PXXX7V582YlJibq0j6Xn7s2M7z22msqWbKkHn744Vy9Lj7+jDwe23VN2ERYWDFduHDeOy5WrLhOnEi2MBEQXE6fPqsLF4remYwOh8Pn56LD4SiSX+fp02f5zMwnTqdDMTGR2T6e7czZqFGj1K5dO9WuXVuzZ8/WyJEj1bRpU/33v/9V8+bNtWbNGrVs2VLvvPNOvgYePXq0du/erUmTJqlYsWK5ei3lDFfy2GPdvX/+5JPPLUwCoKj45ZdteuedUd7x//7vINWqVfRmzpB/rlTOsj1qsU2bNhoyZIgkacqUKfroo480ceJEhYeHa+LEiRo3bpxCQ/2eePPLmDFjtGPHDk2cODHXxQzIjWLFilsdAUARUbt2XTkcDklSiRIlKGbIs2zL2ZQpU/TYY49JkhISEtSgQYOLL/i/s1JatGihlStX5luQ3377TR9++KGOHz+ubt26qUOHDnruuefy7f0BSapevaaqV6+pSZOmWh0FQBFy3XWVJEnPPfeCxUlQFGQ79bV//37t27dPknTttdfq0KFDqly5sqpWrarly5crOjrae1Ha/FCtWjXt3r07394PAICCEhlZStWr12TWDPki23I2f/587d27V9LF65zt3btXlStX1rPPPqs+ffrowoULGjx4cIEFBQAACAbZlrOwsDDvHQH+/ve/e5e3aNFCGzdu1IULFxQRERH4hAAAAEHEr8sYJyQkKCUlRZLkdrsVGxurZcuWcaE9AACAfOZXOXv66af1xx9/SJLGjh2rTz75RFOnTtWoUaOu8EoAAADkhl/l7MCBA6pZs6YkadGiRZoyZYqmT5+ur7/+OqDhAAAAgo1fFypzOp26cOGC9u/fr1KlSum6666Tx+Px7uoEAABA/vCrnDVv3lx9+vRRUlKSHnjgAUnS77//rgoVKgQ0HAAAQLDxq5y98cYbWrBggUJDQ9WhQwdJUmJiov7nf/4noOEAAACCjV/lrFixYuratat3fO7cOf31r3/lFksAAAD5zK8TAkaPHq1t27ZJklatWqVGjRqpYcOGWrFiRUDDAQAABBu/yllsbKyqVasmSZo4caLeeustffDBBxo7dmxAwwEAAAQbv3Zrnj17ViVKlFBiYqLi4uLUunVrSdLhw4cDGg4AACDY+FXOqlatqkWLFungwYNq2rSppIt3DQgPDw9oOAAAgGDjVzkbOnSoRowYodDQUI0YMUKStHr1am9RAwAAQP7wq5zVrVtXs2bN8lnWvn17tW/fPiChAAAAgpVf5UyS1qxZoyVLlighIUGTJk3S9u3bdebMGTVp0iSQ+QAAAIKKX2drzpgxQ8OGDVPVqlW1adMmSVJ4eLjGjRsX0HAAAADBxq9yNn36dE2dOlVPPfWUnM6LL7nxxhu1f//+gIYDAAAINn6Vs5SUFFWsWFGS5HA4JEnp6ekKCwsLXDIAAIAg5Fc5a9iwoSZPnuyz7NNPP1Xjxo0DEgoAACBY+XVCwMsvv6xevXppzpw5SklJUevWrRUREaEPP/ww0PkAAACCyhXLmcfj0d69e/X5559rz549Onz4sCpWrKi6det6jz8DAABA/rhiOXM6nXr22We1detW1a1bV3Xr1i2IXAAAAEHJ72POfvrpp0BnAQAACHp+HXN23XXX6cknn1TLli117bXXes/YlKQ+ffoELBwAAECw8aucpaWl6Z577pEk/fnnnwENBAAAEMz8KmcjR44MdA4AAAAoF/fWPHDggL755hsdP35c5cuX1/3336+qVasGMBoAAEDw8euEgNjYWHXq1Em7d+9WiRIltGfPHnXq1EmxsbGBzgcAABBU/Jo5e/fddzV58mQ1bNjQu2zz5s3q37+/2rVrF7BwAAAAwcbve2veeuutPsvq1aun1NTUgIQCAAAIVn7NnD366KMaM2aM+vbtq+LFi+vcuXMaP368Hn300UDns6XPP/9UcXF/WB0DV+HgwYvft9GjX7M4Ca5WlSrXq3v3f1kdAwACxq9y9vnnn+vkyZOaMWOGSpcurdOnT8sYo3LlyumLL77wPm/VqlWBymkrcXF/aPdvvyskPMrqKMgljztEkvR73EmLk+BquM8lWR0BAALOr3L21ltvBTpHoRMSHqWS17e0OgYQVFL/WG51BAAIOL/KWaNGjbJcfuHCBYWFheVrIAAIVhwyUXhxyEThZ6dDJvw+5mz06NEqX768d9muXbvUv39/LVq0KGDhACCYxMX9oQO/79K1kX5fghI2UVIeSdK5Y79bnARX49iZdKsj+PDrE6BWrVrq0KGDXnnlFd1///2aMmWKPvroI73wwguBzgcAQeXayFA9Wres1TGAoDJ1W4LVEXz4Vc5efPFF3XXXXerfv7/efvttlS9fXnPmzNH1118f6HwAAABBxa/rnEnSoUOHlJKSoujoaKWmpiotLS2QuQAAAIKSX+Wsd+/emjRpkqZMmaJ58+apa9euevjhh/XRRx8FOh8AAEBQ8auclS1bVl999ZXq1q0rSfrnP/+p2bNna+nSpQENBwAAEGz8OuZs2LBhmZbdcMMNmjVrVn7nAQAACGo5zpy9/vrrPuM5c+b4jPv27ZuvYfbv36+uXbuqdevW6tq1qw4cOJCv7w8AAGB3OZaz+fPn+4wvv1PAmjVr8jXM0KFD1b17dy1dulTdu3fXkCFD8vX9AQAA7C7H3ZrGmBzH+Sk+Pl47d+7U1KlTJUlt27bVa6+9poSEBJUta69r/pw6lST3uSRuJQMUMPe5JJ06xQVaARRtOX7KORyOHMf56ejRo6pQoYJCQi7emDokJETly5fX0aNH/S5nMTGRAct3qZAQv69AAiCfhYQ4Va5cKatjBERYWIjOWR0CCFJhYSG2+WzJsZy53W6tX7/eO2OWnp7uM/Z4PIFPmAvx8Wfk8QRudi9DZGRphYSf58bnQAFL/WO5IiNL68SJZKujBMSFC26rIwBB68IFd4F9tjidjhwnlHIsZzExMRo0aJB3HBUV5TPOz92NFStW1J9//im3262QkBC53W4dP35cFStWzLd1AAAA2F2O5WzFihUFlUMxMTGqWbOmFi9erA4dOmjx4sWqWbOm7Y43A4BAOXUqSYln0m13nz+gqDt2Jl3Rp5KsjuFlqyNrhw0bpoEDB+r9999X6dKlNXr0aKsjAQAAFChblbObbrop07XUACBYlCkTpeJnT+rRuuwxAArS1G0JCi8TZXUML047BAAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAbsdXZmoUJ99YsnDzpF2+O4wwNtzgJrob7XJKka6yOAQABRTm7ClWqXG91BFylgwf/kCT9pQo/4Auna/j3B6DIo5xdhe7d/2V1BFyl0aNfkyQNGPCKxUkAAMgax5wBAADYCDNnAGAjx7i3ZqF05rxHkhRZjDmPwujYmXRVtTrEJShnAGATHE9XeB3/v+NZr7mW72FhVFX2+vdHOQMAm+B41sKL41mRn5h/BQAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMAAAAboZwBAADYCOUMAADARihnAAAANkI5AwAAsBHKGQAAgI1QzgAAAGyEcgYAAGAjlDMElRMnjmv37l/17bexVkcBACBLlDMElYSEeEnSl19+YXESAACyZoty9uqrr+q+++5T+/bt1a1bN23fvt3qSCiCFi9e6DNm9gwAYEehVgeQpObNm2vQoEEKCwvTypUr9cILL+g///mP1bGC1po1/9Xq1d9bHSPf7d79q8/4yy+/0M8//2RRmsBp1qyFmjZtbnUMAMBVskU5u+uuu7x/vvXWW3Xs2DF5PB45nbaY2AMAACgwtihnl5o5c6buvPPOqypmMTGRAUgUfDp2bKOOHdtYHSPftWvXLtOyt99+04IkAIqasLAQSVK5cqUsToKioEDKWadOnXTkyJEsH1u7dq1CQi5u1EuWLFFsbKxmzpx5VeuJjz8jj8dcdU4EnxMnkq2OAKAIuHDBLYnPFPjH6XTkOKFUIOVswYIFV3zOsmXLNHbsWE2bNk3XXHNNAaQCAACwH1vs1ly5cqVGjhypqVOnqnLlylbHAQAEQFE92UiSDh78Q5I0evRrFicJHE42Kji2KGcvvfSSwsLC1Lt3b++yadOmKTo62sJUAAD4p0yZMlZHQBFii3K2fv16qyMAAAKsadPmzLwAfuBaFQAA5FFSUqJGjRquU6eSrI6CIoByBgBAHs2dO0t79uzS3LmzrI6CIoByBgBAHiQlJWr9+jWSpHXrVjN7hjyjnAEAkAdz586Sx+ORJHk8HmbPkGeUMwAA8mDDhrU+44xZNOBqUc4AAABshHIGAEAeNG58R45jILcoZwAA5EHnzt3kcDgkSQ6HU126PGRxIhR2lDMAAPIgKipaTZo0kyQ1adJUZcpEWZwIhZ0t7hAAAEBh1rlzN508eYJZM+QLyhkAAHkUFRWtgQOHWB0DRQS7NRE0nM6QHMcAANgB5QxB47bbGuQ4BgDADihnCFr/d3IVAAC2QjlD0Pjxx00+4y1bNmXzTAAArEM5Q9DIuPdddmMAAOyAcgYAAGAjlDMEDcdlB5ldPgYAwA4oZwgaxpgcxwAA2AHlDEHD6XTmOAYAwA746YSgERUV7TOOji5rURIAALJHOUPQSEiI9xnHx5+0KAkAANmjnAEAANgI5QxBIyQkJMcxAFytpKREjRo1XKdOJVkdBUUA5QxBw+125zgGgKs1d+4s7dmzS3PmfGF1FBQBlDMEjeuuq5TjGACuRlJSotatWy1JWrduDbNnyDPKGYLGU0895zN++unnLUoCoCiZO3eW97qJxniYPUOeUc4QNP7yl6oqWTJCklSyZISqVLne4kQAioING9bmOAZyi3KGoJGUlKi0tDRJ0vnzaex6AADYEuUMQSM2doH3z8ZIixbNtzANgKKiceM7fMa3397UoiQoKihnCBrr1q2R250uSXK707Vu3RqLEwEoCjp37ua9HZzT6VTnzt0sToTCjnKGoNGkSVM5nRevbeZ0hqhJE367BZB3UVHR3tmyJk2aqUyZKIsTobCjnCFotGvXScZ4JEnGGLVv/3eLEwEoKjp37iaXqwazZsgXoVYHAAqSw+GQMUYOh9VJABQlUVHRGjhwiNUxUEQwc4agERu7QI7/a2UOh4MTAgAAtkQ5Q9C4eELAxVs2ud1uTggAANgS5QxBo0mTpgoJubgnPyQklBMCAAC2RDlD0GjXrpOczou7NZ1OJycEAABsiXKGoBEVFa1mzVrI4XCoWbPmnO4OALAlztZEUGnXrpMOHz7ErBkAwLYcxhhjdYj8Eh9/Rh5PkflyAABAEeR0OhQTE5n94wWYBbBcUlKiRo0azk3PAQC2ZatytmHDBtWsWVOfffaZ1VFQRM2dO0t79uzS3LmzrI4CAECWbFPOzpw5o7ffflvNmze3OgqKqKSkRO+1zdatW83sGQDAlmxTzkaNGqXHH39c0dHRVkdBETV37izvvTU9Hg+zZwAAW7LF2Zrff/+9kpOTdd9992nVqlVX/T45HVwHbNiw1me8fv0aDRz4okVpAADIWoGUs06dOunIkSNZPvbtt9/qnXfe0dSpU/O8Hs7WRE4ybt106fjEiWSL0gAAgtWVztYskHK2YMGCbB/bvHmzTpw4oS5dukiSEhMTtXLlSiUlJen5558viHgIEk6nUx6Px2cMAIDdWL5bs0GDBlq3bp13PHDgQNWpU0cPP/ywhalQFN1+e1OtXfuDzxgAALth6gBBo3Pnbj7jLl0esigJAADZs3zm7HKjRo2yOgKKMIfDIWOMHA6H1VEAAMgSM2cIGrGxC7ylzOFwaNGi+RYnAgAgM8oZgsa6dWu8JwR4PB7vBWkBALATyhmCxm23NchxDACAHVDOELQ47gwAYEeUMwSNH3/c7DPesmWTRUkAAMge5QxBo06duj7jW26pm80zAQCwDuUMQSMu7g+f8cGDf2TzTAAArEM5Q9D4889jOY4BALADyhmCRkhIaI5jAADsgHKGoOF2p+c4BgDADihnCBrXXVcpxzEAAHZAOUPQeOqp53zGTz/9vEVJAADIHuUMQeMvf6nqnS277rpKqlLleosTASgqkpISNWrUcJ06lWR1FBQBlDMElYce6iGHw6Hu3XtaHQVAERIbu0C//bZbixbNtzoKigDKGYJKxl0CtmzZaHESAEVFUlKiVq/+XsYYrV79X2bPkGeUMwQNPkABBEJs7AJ5PEaS5PF4mD1DnlHOEDQufoB6JEkej5sPUAD5Yt26Nd5L87jd6Vq3bo3FiVDYUc4QNC5+gLolSW63mw9QAPmiSZOm3otah4SEqkmTphYnQmFHOUPQuO22Bj7j+vUbWpQEQFHSrl0nOZ0OSZLT6VT79n+3OBEKO8oZgpYxxuoIAIqAqKhoNWvWQg6HQ82aNVeZMlFWR0Ihx80FETQyztTMbgwAV6tdu046fPgQs2bIF8ycIWhcfhwIx4UAyC9RUdEaOHAIs2bIF5QzBI0WLe72Gd95Z0uLkgAAkD3KGYLGd999k+MYAAA7oJwhaGzYsNZnvH49l9IAANgP5QxBI+MaZ9mNAQCwA8oZgobT6cxxDACAHfDTCUHj9tub5jgGAMAOKGcIGq1a3e8zbt36AYuSAACQPcoZgsb336+Qw3HxFisOh0OrVi23OBEAAJlRzhA01q1b471lkzGGG58DAGyJcoagwY3PAQCFAeUMQYsbnwMA7IhyhqDBjc8BAIUB5QxBo0mTpnI6QyRJTmcINz4HANgS5QxBo127TgoJubjJh4SEqH37v1ucCACAzChnCBpRUdFq1qyFHA6HmjVrrjJloqyOBABAJqFWBwAKUrt2nXT48CFmzQAAtuUwReiUtfj4M/J4isyXAwAAiiCn06GYmMjsHy/ALAAAALgCyhkAAICNUM4AAABsxDYnBMyYMUMzZ85UWFiYnE6nFi5caHUkAACAAmeLcvbdd9/p22+/1dy5cxUZGamTJ09aHQkAAMASttit+cknn+j5559XZOTFMxeuueYaixMBAABYwxYzZ3v37tXPP/+scePG6fz58+rWrZsefPDBXL9PTqelAgAAFAYFUs46deqkI0eOZPnY2rVr5Xa7dfToUX3++edKTEzUQw89pBtuuEENGzbM1Xq4zhkAALC7K13nrEDK2YIFC3J8/LrrrlPbtm3ldDoVExOjO+64Q9u2bct1OQMAACjsbHHMWdu2bfXDDz9IklJTU7VlyxbVqFHD4lQAAAAFzxa3bzp37pxeeeUV7dy5U5LUoUMHPfXUU7l+H3ZrAgAAu7vSbk1blLP8QjkDAAB2Z4tjzgqK0+mwOgIAAECOrtRXitTMGQAAQGFnixMCAAAAcBHlDAAAwEYoZwAAADZCOQMAALARyhkAAICNUM4AAABshHIGAABgI5QzAAAAG6GcAQAA2AjlDAAAwEYoZwh6d999t/bs2WN1DABB6L333tP58+ev+rWjR4/O50SwA8oZkI309HSrIwAo4iZMmKALFy5k+RifQcEr1OoAwNatW/Xmm28qJSVFktS/f3+VLl1ab7zxhlJTU1WyZEkNHjxYdevW1aFDh/SPf/xDDz74oH744QedO3dOb7/9tmbNmqWff/5Z4eHhev/991WuXLls17d582a9+uqrkqSGDRvKGON97O6779YDDzyg9evXy+VyafDgwXr99de1fft2SVKHDh305JNPSpJ+//13vfTSSzp79qxq1KihgwcP6plnntFdd92lTz75REuWLJHb7Vbx4sU1bNgw1axZM1B/hUDQKujPj7179+qNN97QiRMnJEmPPfaYOnXqpD/++ENDhgxRQkKCQkND9cILL6h58+aSpOrVq+uFF17QsmXLlJSUpP79+6t169bez6Fu3brJ6XRqxowZGjFihEJCQrR//36lpKRo4cKFmjx5shYtWiRJuuWWW/Tyyy8rIiIiU7bJkyfru+++k9vtVoUKFfTaa6/l+LXAxgxgocTERHPHHXeYLVu2GGOMSU9PNydOnDAtWrQwa9euNcYYs2bNGtOiRQuTlpZm4uLijMvlMitXrjTGGDNlyhRTv359s3PnTmOMMUOHDjVjxozJdn1paWmm0fMJRgAAC9FJREFUWbNmZv369cYYY5YsWWJcLpfZvXu3McaYu+66ywwdOtT7/DfffNP079/feDwek5ycbB544AGzatUqY4wxnTp1Ml999ZUxxpht27aZGjVqmBUrVhhjjImPj/e+x5o1a0yXLl3y+lcF4DIF/flx4cIF06pVK/P11197lyUkJBhjjOncubP58ssvjTHG/Pbbb6ZRo0bezwGXy2VmzJhhjDFm8+bNplmzZt7Xu1wuc+bMGe94wIABplOnTiYlJcUYY8yqVatMmzZtTHJysvF4PObFF180b775pjHGmPHjx5tRo0YZY4z56quvzMsvv2zcbrcxxpiZM2eaf//737n+O4U9sFsTlvrpp59000036bbbbpMkhYSEKD4+XmFhYWrSpIkk6Y477lBYWJj2798vSSpZsqTuvPNOSVLt2rV17bXXemelateurYMHD2a7vn379qlEif/X3t0HRVX1ARz/7gIr0lIgJkHOmGUYyeiugbRorqAz6VKAiQyNWskf4suINeXYOI2TSaQOUcmEZZI2OcwUYjnGy9T4UhRI45RUjmQyTiGvM4rJAu66y3n+YLyPG1ACCfs88/v8dfeec8/97fnjzO+ec1/GEhsbC4DNZiMwMNCjTkpKirZdXV3N0qVL0el0GI1GEhMTqa6uxm63c+7cOZ588kmg92p26tSp2nG//PILy5Yt44knnuCNN97g7Nmzw+kmIUQ/Rnr8uHDhAi6Xi0WLFmn7goODsdvtnD17liVLlgAwZcoUIiMjOX36tFbPZrMBYDKZaGtrw+FwDHiehQsXEhAQAPSOQTabDaPRiE6nIy0tjerq6j7HHDt2jKqqKhYvXkxycjJFRUU0Njb+fQcKryXLmuJ/jsFg0Lb1er3Hbx8fH9xu96Da0+l0Hr9vDIpDORbA6XSyYcMGDhw4wLRp02htbdWWN4QQo+vfHj9u1ZgxY7RzQO/9ZDf2/dVgxqAblFKsWbOG1NTUoQcpvIbMnIlRZTKZqK+v58cffwTA7XYTEhLC9evXOXnyJNB75ehyuZg8efKwz3f//fdz7do1Tp06BUBFRQVXr14dsL7FYqGkpASlFHa7nbKyMuLi4jAajTz44IN88cUXAJw5c0Z74tPpdOJyuQgLCwOgqKho2HELIfoa6fFj8uTJ+Pr6Ul5eru1rb2/HaDQSGRnJZ599BvTel1ZXV4fJZPrHNu+44w7sdvuA5RaLhfLycux2O0opDh48SFxcXJ96CQkJFBUV8eeffwK941BdXd1g/6LwEjJzJkZVUFAQ+fn5bN++na6uLvR6PZs2bWLXrl0eN/S+8847Hle4Q2UwGMjLy/N4ICA8PHzA+mvXrmXbtm3a8mVSUpI2C7Zjxw42b97Mnj17iIiIICIigsDAQIxGI1lZWaSmphIUFMTjjz8+7LiFEH2N9Pjh6+tLQUEBr732GgUFBeh0OjIyMkhJSSE3N5ctW7awf/9+fH192blzJ+PGjfvHNjMyMnjmmWfw9/fn448/7lNutVr59ddfSU9PByAqKoo1a9b0qZeSksKVK1dYvnw50DuT9vTTT/PQQw8N81+L0aBT6qZH1YQQt6yzs5OAgAB0Oh3nz59nxYoVVFRUcNddd412aEIIIf6HycyZEEN04xH+G9c327Ztk8RMCCHEsMnMmfi/VFxczIEDB/rs3759u7xvTAjxt2T8EKNNkjMhhBBCCC8iT2sKIYQQQngRSc6EEEIIIbyIJGdCiNsiMTGRmpqa236e/Px8Xnrppdt+HiGEGCnytKYQYkjMZrO23d3djcFg0N5+vnXrVkpLS0crtFuWkJBAdnZ2vy/1vFUvv/wyoaGhvPDCC/9iZP8Ob45NCDEwSc6EEENy463s8O8kOd7I5XLh6yvDpBBiZMmyphDitkhISKCqqgroXXrMysri+eefx2w2s3jxYo9Py9TX17NixQqio6NJTEzk6NGjA7bb0NDA8uXLMZvNrFy5kvb2do/y06dPk56eTnR0NElJSQMurW7cuJGmpiZWr16N2Wzmgw8+4OLFi0ydOpXi4mLmzZvHs88+C0BWVhazZ8/mkUceYdmyZfz2228AfPLJJxw5coTCwkLMZjOrV68GYM+ePSxYsACz2YzNZuOrr77Sznvo0CHS09PJyckhOjqa+fPn88MPP3Do0CGsVisWi0X7DBD0foZnx44dzJs3j7i4OLZs2cK1a9cAqKmpYe7cuXz44YdYLBbmzJlDSUnJ38Y2mL4WQowSJYQQwxQfH6++++67Afft2rVLPfzww6q8vFw5nU61d+9eFR8fr5xOp3I6nWrBggVq9+7dyuFwqKqqKmUymVR9fX2/50pLS1M5OTnK4XCo77//XplMJvXiiy8qpZRqaWlRs2bNUidOnFBut1t9++23atasWerSpUu3FHdDQ4OKiIhQGzduVJ2dnaq7u1sppVRxcbHq6OhQDodDZWdnq6SkJO2YTZs2qby8PI92y8rKVEtLi3K73aq0tFTNmDFDtba2KqWUKikpUZGRkergwYPK5XKpvLw8ZbVa1auvvqocDoeqrKxUJpNJ2e12pZRSr7/+usrMzFTt7e2qo6NDZWZmqtzcXKWUUidPnlSRkZHq7bffVk6nU504cUJNnz5dXblypd/YBtvXQojRITNnQogRMW3aNBYuXIifnx8rV67E6XRSW1tLbW0tXV1drFq1CoPBgMViIT4+vt971pqamvj555/ZsGEDBoOBmJgYEhIStPLDhw8zd+5crFYrer2e2bNnExUVxddffz2oWNevX09AQAD+/v4ApKamYjQaMRgMrF+/nrq6Ojo6OgY8ftGiRYSGhqLX67HZbEyaNImffvpJK584cSJLlizBx8cHm81Gc3Mz69atw2AwMGfOHAwGA3/88QdKKT799FM2b95MUFAQRqORzMxMj77x9fVl3bp1+Pn5YbVaCQgI4MKFC/3GNZi+FkKMHrmZQggxIu655x5tW6/XExoaSltbm1am1//3WjE8PJzW1tY+bbS1tXHnnXcSEBDgUbe5uRnoTd4qKio4fvy4Vu5yuYiNjR1yrG63m7feeouKigouX76sxdne3k5gYGC/x3/++efs27ePxsZGALq6ujyWX0NCQrTtGwng+PHjtX1jxoyhs7OTy5cv093dzVNPPaWVKaXo6enRfgcFBXncFzd27Fi6urr6jautre2W+1oIMXokORNCjIiWlhZtu6enh9bWViZMmKCV9fT0aElDc3Mz9913X5827r77bq5evUpXV5eWoDU1NaHT6QAICwsjOTmZ7OzsYcV6oz2AI0eOcPToUfbt28fEiRPp6OggJiZG+6bqzXUBGhsbeeWVV9i/fz9msxkfHx+Sk5OHFEdwcDD+/v6UlpYSGho6rP8BMGHChFvuayHE6JFlTSHEiDhz5gxffvklLpeLjz76CIPBwIwZM5g+fTr+/v7s3buX69evU1NTw7Fjx7DZbH3auPfee4mKiiI/Px+n08mpU6c8ZsmSkpI4fvw4lZWVuN1uHA4HNTU1HonhzcaPH09DQ8Pfxt3Z2YnBYCA4OJju7m7y8vI8ykNCQrh48aL2u7u7G51Ox7hx4wAoKSnRHiAYLL1ez9KlS8nJyeHSpUsAtLa2UllZeUvH/zW2wfS1EGL0SHImhBgR8+fPp6ysjJiYGA4fPkx+fj5+fn4YDAbee+89vvnmGx599FG2bt3Kzp07eeCBB/pt580336S2tpbY2FjeffddUlJStLKwsDAKCgp4//33sVgsWK1WCgsLPZYBb7Zq1Sp2795NdHQ0hYWF/dZJSUkhPDycxx57jMTEREwmk0d5amoq58+fJzo6mrVr1zJlyhQyMjJIT08nLi6Oc+fOMXPmzCH2Wu9TpZMmTSItLY2ZM2fy3HPPDXhP2V/9NbbB9rUQYnTIh8+FELddfn4+v//+O7m5uaMdihBCeD2ZORNCCCGE8CKSnAkhhBBCeBFZ1hRCCCGE8CIycyaEEEII4UUkORNCCCGE8CKSnAkhhBBCeBFJzoQQQgghvIgkZ0IIIYQQXkSSMyGEEEIIL/If9OuIhcBAbtAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFM6B-q95uCO"
      },
      "source": [
        "## Desafios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VQtyzvi53m0"
      },
      "source": [
        "###Desafio 01: Ordenar o gráfico countplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "z_mpDj3extdt",
        "outputId": "6e672da4-14f5-4722-9bfb-4f389a89296f"
      },
      "source": [
        "# Configura o tamanho do gráfico\n",
        "plt.figure(figsize=[10, 8])\n",
        "sns.countplot(x='composto', data=dados_compostos, order=cod_compostos)\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "45-C6JYaBDO5"
      },
      "source": [
        "###Desafio 2: Melhorar a visualização do gráfico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "YOrje9Yl6HdY",
        "outputId": "49808cc5-0e45-4129-abc3-fb4bb2cfc250"
      },
      "source": [
        "plt.figure(figsize=[10, 8])\n",
        "sns.set_palette(\"rocket\")\n",
        "ax = sns.countplot(x='composto', data=dados_compostos, order=cod_compostos)\n",
        "ax.set_title('Compostos mais usados', fontsize=25)\n",
        "ax.set(xlabel='Código do composto', ylabel='Frequencia')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tARi0RmwMGwD"
      },
      "source": [
        "###Desafio 03: Plotar os histogramas com o seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "xmOWTFdBQdpb",
        "outputId": "12d191cc-4e48-4f3a-fac5-a4a85011feb7"
      },
      "source": [
        "sns.histplot(data=dados.loc[0:,'g-0':'g-771'].describe().T, x='mean', bins=50)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcaaf743fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "n706IDmWQZT7",
        "outputId": "ab5a5e52-f5fa-4038-e9aa-fb0c84d625ed"
      },
      "source": [
        "sns.histplot(data=dados.loc[0:,'c-0':'c-99'].describe().T, x='mean', bins=30)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcaaf654410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEMCAYAAAArnKpYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcMUlEQVR4nO3de3BU5f3H8c9udhOBhIYkSxogSnEKggo4BOOdmoAgBCJ2UhTBMRUYxQv+IWIxynWgMQ4DIyI41k5HGew4VhigcrFgrLRm0II2JopisBlDSLJcA5jL7vn94S+nUHI5IbvnLOz79Rc5+/Cc7/ecTT67Z3efdRmGYQgAAElupwsAAEQOQgEAYCIUAAAmQgEAYCIUAAAmQgEAYCIUAAAmj9MFhMKxY6cVDIbv4xbJyfHy++vDNr9d6COy0EdkuRz6sNqD2+1Sr149Wr3tsgiFYNAIayi07ONyQB+RhT4iy+XQR1d74PIRAMBEKAAATIQCAMBEKAAATLaEQmFhobKysjRo0CAdOHDA3N7Q0KAFCxborrvu0sSJE/X888/bUQ4AoA22vPsoOztbDz74oB544IHzthcVFSkuLk7bt2+Xy+VSXV2dHeUAANpgSyhkZGRcsO306dPauHGjiouL5XK5JEkpKSl2lAMAaINjn1OorKxUYmKiVq9erZKSEvXo0UNz5sxpNUA6kpwcH4YKz+fzJYR9H3aw2kdTc1BeT8dXF62OC7VoOx+Rjj4iR1d7cCwUAoGAKisrNWTIEM2bN0+ff/65HnnkEe3cuVPx8Z37I+/314f1Qyc+X4Jqa0+FbX67dKYPny9Bc1cVdziuaM4o249NNJ6PSEYfkcNqD263q80H0469+ygtLU0ej0c5OTmSpGHDhqlXr16qqKhwqiQAiHqOhUJSUpIyMzO1Z88eSVJFRYX8fr+uuuoqp0oCgKhny+WjpUuXaseOHaqrq1N+fr4SExO1detWLVq0SPPnz1dhYaE8Ho9efPFF9ezZ046SAACtsCUUCgoKVFBQcMH29PR0vfnmm3aUAACwgE80AwBMhAIAwEQoAABMhAIAwEQoAABMhAIAwEQoAABMjq19hMtDU3PQ0gJcDY3NOnnirA0VAegKQgFd4vW4LS+cByDycfkIAGAiFAAAJkIBAGAiFAAAJkIBAGAiFAAAJkIBAGCyLRQKCwuVlZWlQYMG6cCBAxfcvnr16jZvAwDYw7ZQyM7O1vr169W3b98Lbvvyyy+1f//+Vm8DANjHtlDIyMhQWlraBdsbGxu1ePFiLVy40K5SAABtcPw1hVWrVmnSpEnq16+f06UAQNRzdO2jffv2qbS0VE8//XSX5klOjg9RRW2zsujbpaAzfXi91u4eVseF8hhG4/mIZPQRObrag6OhsHfvXh08eFDZ2dmSpOrqaj388MNavny5brvtNsvz+P31CgaNcJUpny9BtbWnwja/XTrTh8+XoKamZktjrY4L1TGMxvMRyegjcljtwe12tflg2tFQmDVrlmbNmmX+nJWVpbVr12rgwIEOVgUA0cu21xSWLl2qO+64Q9XV1crPz9eECRPs2jUAwCLbnikUFBSooKCg3TG7du2yqRoAQGscf/cRACByEAoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAw2RYKhYWFysrK0qBBg3TgwAFJ0rFjxzRz5kyNHTtWEydO1OOPP66jR4/aVRIA4H/YFgrZ2dlav369+vbta25zuVyaMWOGtm/frs2bNys9PV0vvfSSXSUBAP6HbaGQkZGhtLS087YlJiYqMzPT/Hn48OGqqqqyqyQAwP+ImNcUgsGgNmzYoKysLKdLAYCo5XG6gBZLlixR9+7dNW3atE7/3+Tk+DBUdD6fLyHs+7BDZ/rweq3dPayOC+UxjMbzEcnoI3J0tYeICIXCwkJ9//33Wrt2rdzuzj958fvrFQwaYajsJz5fgmprT4Vtfrt0pg+fL0FNTc2WxlodF6pjGI3nI5LRR+Sw2oPb7WrzwbTjobBixQqVlpbqtddeU2xsrNPlAEBUsy0Uli5dqh07dqiurk75+flKTEzUypUrtW7dOvXv31/33XefJKlfv3565ZVX7CoLAHAO20KhoKBABQUFF2z/+uuv7SoBANCBiHn3EQDAeYQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATLaEQmFhobKysjRo0CAdOHDA3F5RUaEpU6Zo7NixmjJlig4dOmRHOQCANtgSCtnZ2Vq/fr369u173vYFCxZo6tSp2r59u6ZOnaoXXnjBjnIAAG2wJRQyMjKUlpZ23ja/36+ysjLl5ORIknJyclRWVqajR4/aURIAoBWOvaZw+PBhpaamKiYmRpIUExOj3r176/Dhw06VBABRz+N0AaGQnBwf9n34fAlh34cdOtOH12vt7mF1XCiPYTSej0hGH5Gjqz04FgppaWk6cuSIAoGAYmJiFAgEVFNTc8FlJiv8/noFg0YYqvyJz5eg2tpTYZvfLp3pw+dLUFNTs6WxVseF6hhG4/mIZPQROaz24Ha72nww7djlo+TkZA0ePFhbtmyRJG3ZskWDBw9WUlKSUyUBQNSz5ZnC0qVLtWPHDtXV1Sk/P1+JiYnaunWrFi5cqGeffVZr1qxRz549VVhYaEc5AIA22BIKBQUFKigouGD71VdfrXfeeceOEgAAFli+fPT++++3un3btm0hKwYA4CzLofDcc8+1up0PnAHA5aPDy0eVlZWSJMMwzH+fe1tsbGx4KgMA2K7DUBgzZoxcLpcMw9CYMWPOuy0lJUVPPPFE2IoDANirw1D46quvJEnTpk3TW2+9FfaCAADOsfyaAoEAAJc/y29Jrays1MqVK1VeXq4zZ86cd9uHH34Y6roAAA6wHApPP/200tPTNW/ePHXr1i2cNQEAHGI5FL755htt2LBBbjdf1haJev6sm+JiOz6dib26y+uJsaEiAJciy6EwcuRIlZWV6brrrgtnPbhIcbEezV1V3O4Yr9ejZbNv7XCcJBXNGRWq0gBcQiyHQt++fTVjxgyNGTNGKSkp5902Z86ckBcGALCf5VA4e/as7rzzTjU3N6u6ujqcNQEAHGI5FJYvXx7OOgAAEaBTb0ltS3p6ekiKAQA4y3IonLvcRQuXyyVJKi8vD31lAADbWQ6FluUuWtTW1mr16tXKyMgIeVEAAGdc9IcOfD6fnnvuOa1YsSKU9QAAHNSlb1777rvvdPbs2S4XsXv3bq1atUqGYcgwDD3++OO66667ujwvAKBzLIfC1KlTzdcQpJ/eovrtt9/qscce61IBhmHomWee0fr16zVw4EB99dVXuv/++zV69Gg+PQ0ANrMcCnl5eef93K1bN11zzTXq379/l4twu906deqUJOnUqVPq3bs3gQAADrAcCpMnTw5LAS6XSytXrtTs2bPVvXt3nT59Wq+99lpY9gUAaJ/lUGhqatKrr76qTZs2qaamRr1791Zubq4eeeSRLn0lZ3Nzs9atW6c1a9ZoxIgR+uyzz/TUU09p69at6tGjh6U5kpPjL3r/Vvl8CWHfR1d5vdZOp1PjrBzDpuagvJ6OnyX6fAmWx0ayS+F+ZQV9RI6u9mA5FIqKivTFF19o0aJF6tOnj6qqqrRmzRrV19dr/vz5F11AeXm5ampqNGLECEnSiBEj1K1bNx08eFBDhw61NIffX69g0Oh44EXy+RJUW3sqbPOHgs+XoKam5nbHtPzx7mhci1CPs3IMfb4ESwv7NTU1q2jOqIg/L+25FO5XVtBH5LDag9vtavPBtOVQ2LZtmzZt2qRevXpJkgYMGKAhQ4YoNze3S6Hw85//XNXV1fruu+80YMAAHTx4UH6/X1deeeVFzwkAuDiWQ+HcTzJb2W6Vz+fTwoULNWfOHPPdTcuWLVNiYmKX5gUAdJ7lUBg3bpweffRRPfbYY+rTp49++OEHvfrqqxo3blyXi5g0aZImTZrU5XkAAF1jORTmzp2rV199VYsXL1ZNTY1SU1M1YcIEPfroo+GsDwBgow7fuvHZZ5+pqKhIsbGxmjNnjnbu3KnPP/9cO3bsUGNjo8rKyuyoEwBggw5DYd26dRo5cmSrt2VmZmrt2rUhLwoA4IwOQ6G8vFy33357q7fdcsstKi0tDXlRAABndBgK9fX1ampqavW25uZmnT59OuRFAQCc0WEoDBgwQB9//HGrt3388ccaMGBAyIsCADijw1B46KGHtGDBAu3YsUPBYFCSFAwGtWPHDi1cuFD5+flhLxIAYI8O35I6ceJE1dXVad68eWpqalJiYqKOHz8ur9erJ598Ujk5OXbUCQCwgaXPKeTn5ysvL0/79u3T8ePHlZiYqBtuuEHx8eFfiA4AYB/LH16Lj49v811IAIDLw6W97jAAIKQIBQCAiVAAAJgIBQCAiVAAAJgIBQCAiVAAAJgsf04hnBoaGrRs2TL985//VFxcnIYPH64lS5Y4XRYARJ2ICIWioiLFxcVp+/btcrlcqqurc7okAIhKjofC6dOntXHjRhUXF8vlckmSUlJSHK4KAKKT468pVFZWKjExUatXr9a9996r6dOn69NPP3W6LACISo4/UwgEAqqsrNSQIUM0b948ff7553rkkUe0c+dOywvuJSeHf2E+ny8h7PvoKq/X2ul0apzVY2hlPq/Xo6bmoKU5m5qD8nocf/zTqkvhfmUFfUSOrvbgeCikpaXJ4/GYS3APGzZMvXr1UkVFha6//npLc/j99QoGjbDV6PMlqLb2VNjmDwWfL0FNTc3tjmn5Y9vRuBahHmflGFrto6mpWV6PW3NXFXc4Z9GcURF5/i6F+5UV9BE5rPbgdrvafDDt+MOnpKQkZWZmas+ePZKkiooK+f1+XXXVVQ5XBgDRx/FnCpK0aNEizZ8/X4WFhfJ4PHrxxRfVs2dPp8sCgKgTEaGQnp6uN9980+kyACDqOX75CAAQOQgFAICJUAAAmAgFAICJUAAAmAgFAICJUAAAmAgFAIApIj68BlyOev6sm+Ji2/8V8/kS1NDYrJMnztpUFdA+QgEIk7hYT7sL9rUs7Fc0Z5SNVQHt4/IRAMBEKAAATIQCAMBEKAAATIQCAMBEKAAATIQCAMAUUaGwevVqDRo0SAcOHHC6FACIShETCl9++aX279+vvn37Ol0KAEStiAiFxsZGLV68WAsXLnS6FACIahERCqtWrdKkSZPUr18/p0sBgKjm+NpH+/btU2lpqZ5++umLniM5OT6EFbXO50sI+z66yuu1djqdGNfUHLR8DK3M1zLGao1W9t3UHJTX0/HjpKbmgLyeGEv77ag+r9dj+dhYrc8Jl8LvhxWXQx9d7cHxUNi7d68OHjyo7OxsSVJ1dbUefvhhLV++XLfddpulOfz+egWDRthq9PkSVFt7Kmzzh4LPl6CmpuZ2x7T8gepoXItQjvN63O0uDteiaM4oS320jLFao5Xz5/MlWK4xFL209NGZYxOJ98NL4ffDisuhD6s9uN2uNh9MOx4Ks2bN0qxZs8yfs7KytHbtWg0cONDBqgAgOkXmc1EAgCMcf6bwv3bt2uV0CQAQtXimAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwRdznFOzU82fdFBdrbd0eJ/bb0NiskyfOhnTf0agz6y4B0S6qQyEu1mN5zZnLYb/RqjNrCwHRjstHAAAToQAAMBEKAAAToQAAMBEKAAAToQAAMBEKAAAToQAAMDn+4bVjx47pmWee0X/+8x/Fxsbqqquu0uLFi5WUlOR0aQAQdRx/puByuTRjxgxt375dmzdvVnp6ul566SWnywKAqOR4KCQmJiozM9P8efjw4aqqqnKwIgCIXo5fPjpXMBjUhg0blJWV1an/l5wcf9H79HqtLYhnZUG1puagvB5rOWtlv5I6tZCb1Tkvh3EtYyK5RivjOttHpC7sF6l1ddbl0EdXe4ioUFiyZIm6d++uadOmder/+f31CgaNTu/P50tQU1Nzh+M6s6Babe2pkO1XkqX5rM7Z8ofH6r4jdZzX6zHHRGqNVsZdTB9W7w928vkSIrKuzroc+rDag9vtavPBdMSEQmFhob7//nutXbtWbrfjV7UAICpFRCisWLFCpaWleu211xQbG+t0OQAQtRwPhW+++Ubr1q1T//79dd9990mS+vXrp1deecXhygAg+jgeCr/85S/19ddfO10GAEAR8JZUAEDkIBQAACZCAQBgIhQAACZCAQBgIhQAACZCAQBgcvxzCpcTqwvnOTUfLm2duT80NDbr5ImzIdt3z591U1xs238uWuoK9X6jUUfHukW4jjWhEEKdWTgvlPN1Zk5cupy8P8TFetrc97kL+3E/7Lr2jvW5wnWsuXwEADARCgAAE6EAADARCgAAE6EAADARCgAAE6EAADARCgAAU0SEQkVFhaZMmaKxY8dqypQpOnTokNMlAUBUiohQWLBggaZOnart27dr6tSpeuGFF5wuCQCikuPLXPj9fpWVlemPf/yjJCknJ0dLlizR0aNHlZSUZGkOt9t10fvvlRB3WYyzMtbj9YRl33aP83g9am6KcWTfoRwXzj6krv1edGbf5/YRjv3aKVJqt3qeW6vXSg/tjXEZhmFY2nuYlJaWat68edq6dau5bfz48SoqKtK1117rYGUAEH0i4vIRACAyOB4KaWlpOnLkiAKBgCQpEAiopqZGaWlpDlcGANHH8VBITk7W4MGDtWXLFknSli1bNHjwYMuvJwAAQsfx1xQk6eDBg3r22Wd18uRJ9ezZU4WFhRowYIDTZQFA1ImIUAAARAbHLx8BACIHoQAAMBEKAAAToQAAMBEKrTh79qyeeuopjRkzRuPGjdPu3bvbHFteXq4HHnhA48eP1/jx41VcXGxjpe2z2kdJSYmGDRum3Nxc5ebmKi8vz+ZK29eZ8yFJDQ0NmjBhgu69916bKuyY1R7Ky8s1efJk5ebmasKECXr++efV2Nhoc7Vts9rHBx98oHvvvVc5OTmaMGGC3njjDZsrbZ/VPo4cOaLp06drxIgREXV/srKIaCAQ0KJFizR69GiNGTNG77zzjrXJDVzg5ZdfNp577jnDMAyjoqLCuOWWW4z6+voLxp0+fdrIysoy9u3bZxiGYTQ1NRlHjx61tdb2WO3jk08+MSZPnmx3eZZZ7aPF8uXLjd/97ncR1ZPVHs6ePWs0NDQYhmEYgUDAePzxx40//elPttbaHqt97N+/36iurjYMwzBOnjxpjB492ti7d6+ttbbHah8nT5409u7da+zevTui7k/Tp083Nm7caBiGYWzcuNGYPn36BWPee+8947e//a0RCAQMv99v3H777UZlZWWHc/NMoRXvv/++pkyZIknq37+/rrvuOn300UcXjNuyZYtGjBih4cOHS5I8Ho969epla63tsdpHpOtMH59++qkOHTqk3NxcO0vskNUerrjiCsXGxkqSmpub9eOPP8rtjpxfU6t9DBs2TKmpqZKkhIQEXX311frhhx9srbU9VvtISEhQRkaGunXrZneJbWpZRDQnJ0fST4uIlpWV6ejRo+eN++tf/6q8vDy53W4lJSVp9OjR2rZtW4fzR869LYJUVVWpb9++5s9paWmqrq6+YNy3334rj8ejmTNnKjc3V/Pnz9eJEyfsLLVdVvuQpEOHDmny5MnKy8vTe++9Z1eJlljt48yZM1q2bJkWLVpkZ3mWdOZcHDlyRLm5ucrMzFSPHj30m9/8xq4yO9SZPlocPHhQ+/fv10033RTu8iy7mD4ixeHDh5WamqqYmJ9Wpo2JiVHv3r11+PDhC8b16dPH/Nlqj44vne2EyZMnq6qqqtXb/vGPf1ieJxgM6pNPPtHbb7+tlJQULV++XL///e+1fPnyUJXarlD1ce2116q4uFgJCQmqrKxUfn6+UlNTdcstt4Sq1HaFqo8XX3xRU6dOVWpqqu1f1BSqHiQpNTVVmzZt0pkzZzR37lzt3LlTEyZMCEWZHQplH5JUU1Oj2bNna8GCBeYzBzuEuo9oEpWh0NEj4T59+uiHH34w1186fPiwMjMzLxiXlpamzMxM9e7dW5I0ceJEzZ8/P/QFtyFUfcTHx5v/Tk9P1+jRo/Wvf/3LtlAIVR+fffaZPvroI61Zs0YNDQ06ceKEJk6cqM2bN4el7nOFqodzde/eXePHj9fmzZttC4VQ9uH3+5Wfn68ZM2bo7rvvDnmt7QnH+YgU5y4iGhMT0+YiomlpaaqqqtLQoUMlXfjMoS1cPmrFuHHj9Oc//1nST5dV/v3vf+v222+/YNzdd9+tL774QvX19ZKkjz76SIMGDbK11vZY7aOmpkbG/692cvz4ce3Zs0fXXHONrbW2x2ofmzdv1q5du7Rr1y6tWLFCAwcOtCUQrLDaQ2Vlpfluo8bGRv3tb3/TwIEDba21PVb7OHbsmPLz8/XAAw9E3LvZJOt9RCKri4iOGzdO77zzjoLBoI4ePaoPPvhAY8eO7XB+1j5qxZkzZ/Tss8+qvLxcbrdbc+fO1ejRoyVJq1atUu/evXX//fdLkjZu3KjXX39dLpdL/fr105IlS5SSkuJk+Sarfbz11lvasGGDPB6PAoGA7rnnHs2YMcPh6v+rM+ejRUlJiQoLC/WXv/zFiZIvYLWHTZs2mfenYDCokSNHat68ebriiisc7uAnVvsoLCzU+vXr9Ytf/ML8vw8++KB+/etfO1X6eaz2EQgEdOedd6qxsVH19fVKSkpSXl6ennjiCUfrb2sR0ZkzZ+rJJ5/U9ddfr0AgoMWLF2vPnj2SpJkzZ5ovrreHUAAAmLh8BAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhAAAwEQoAABOhALQhKytLr7/+uiZOnKjhw4dr/vz5qqur04wZM3TDDTfooYceMlfF3b9/v+677z5lZGRo0qRJKikpMed59913dffdd+uGG25Qdna23n77bfO2kpIS3XHHHXrjjTd0880367bbbtO7775re6+AKZRf/ABcTu68804jLy/PqK2tNaqrq42bbrrJuOeee4wvv/zS+PHHH43p06cbL7/8slFdXW3ceOONxocffmgEAgHj448/Nm688UbD7/cbhmEYu3fvNr7//nsjGAwaJSUlxtChQ43S0lLDMH76gqPBgwcbK1euNBobG40PP/zQGDp0qHH8+HEnW0cU45kC0I5p06YpJSVFqampysjI0NChQzVkyBDFxcVpzJgxKisr06ZNm3THHXdo1KhRcrvduvXWW3XdddeZX836q1/9SldeeaVcLpduvPFG3Xrrrfr000/NfXg8Hj322GPyer0aNWqUunfvroqKCqdaRpSLyqWzAavOXdwwLi7uvJ+vuOIKnTlzRlVVVdq2bdt53/Pb3NxsLsVcXFysV155RYcOHVIwGNSPP/543sqniYmJ8nj++6vYrVs3nTlzJpxtAW0iFIAuSktLU25urpYuXXrBbY2NjXryySdVWFio7Oxseb1ezZ4921yqHIg0XD4CumjSpEnavXu3/v73vysQCKihoUElJSWqrq5WY2OjGhsblZSUJI/Ho+LiYnMpYyASEQpAF6WlpWnNmjVat26dbr75Zo0aNUp/+MMfFAwGFR8fr4KCAj311FMaOXKktmzZoqysLKdLBtrE9ykAAEw8UwAAmAgFAICJUAAAmAgFAICJUAAAmAgFAICJUAAAmAgFAICJUAAAmP4PpqLQ71/tnFEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}