Imersão_Dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Imersão_Dados.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "y8T4kxtSAXbF"
      ],
      "authorship_tag": "ABX9TyOjc17jbCD10KBawp+F1ldW",
      "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/axeljunior/Alura-ImersaoDados/blob/main/Imers%C3%A3o_Dados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WnC-vdzgvnZ2"
      },
      "source": [
        "## Carregando bibliotecas e a base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNbKRP-rAj3Z"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "urlBaseDeDados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "dados = pd.read_csv(urlBaseDeDados, compression = 'zip')\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eZYgjJfiNdzB",
        "outputId": "909cf9ab-ebe3-4fca-8938-b96952706987"
      },
      "source": [
        "print('====== As 5 primeiras linhas do DataFrame ======')\n",
        "dados.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "====== As 5 primeiras linhas do DataFrame ======\n"
          ],
          "name": "stdout"
        },
        {
          "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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8ITVMopmNinl",
        "outputId": "089a7210-a4ce-4543-cea5-a41d60b46d6d"
      },
      "source": [
        "print('====== As 5 ultimas linhas do DataFrame ======')\n",
        "dados.tail()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "====== As 5 ultimas linhas do DataFrame ======\n"
          ],
          "name": "stdout"
        },
        {
          "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>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>0.4024</td>\n",
              "      <td>-0.0895</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1410</td>\n",
              "      <td>1.2640</td>\n",
              "      <td>-0.8663</td>\n",
              "      <td>0.8129</td>\n",
              "      <td>-0.1514</td>\n",
              "      <td>-0.4652</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.9170</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</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",
              "23809  id_fffb1ceed     com_droga     24   D2  ...  0.0631  0.9171  0.5258  0.4680\n",
              "23810  id_fffb70c0c     com_droga     24   D2  ... -0.2084 -0.1224 -0.2715  0.3689\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "23813  id_ffffdd77b     com_droga     72   D1  ... -3.5770 -0.4775 -2.1500 -4.2520\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y8T4kxtSAXbF"
      },
      "source": [
        "# Dados Gerais das colunas *Dose*, *Tratamentos* e *Tempo*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VR_u2Ae8_bTc",
        "outputId": "97cef5be-ac3f-45a5-8145-f5d6caa651ea"
      },
      "source": [
        "print('====== Dados nas coluna ======')\n",
        "print('Tratamento   |', dados['tratamento'].unique())\n",
        "print('Tempo        |', dados['tempo'].unique())\n",
        "print('Dose         |', dados['dose'].unique())\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "====== Dados nas coluna ======\n",
            "Tratamento   | ['com_droga' 'com_controle']\n",
            "Tempo        | [24 72 48]\n",
            "Dose         | ['D1' 'D2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "s23ct1rxKMbi",
        "outputId": "8adef3a4-38ef-4efa-b804-7b2ea30ce72c"
      },
      "source": [
        "tratamento1 = dados['tratamento'].unique()[0].replace('_',' ')\n",
        "tratamento2 = dados['tratamento'].unique()[1].replace('_',' ')\n",
        "\n",
        "print('Nº de culturas que foram tratadas', tratamento1,'=', dados['tratamento'].value_counts().get('com_droga'))\n",
        "print('Nº de culturas que foram tratadas', tratamento2,'=', dados['tratamento'].value_counts().get('com_controle'))\n",
        "\n",
        "print('\\n')\n",
        "print(dados['tratamento'].value_counts(normalize=True))\n",
        "print('\\n')\n",
        "\n",
        "labels = tratamento1, tratamento2\n",
        "plt.title('Tipos de tratamento')\n",
        "plt.pie(dados['tratamento'].value_counts(), labels=labels, radius=1.2, autopct='%1.1f%%',pctdistance=0.71)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de culturas que foram tratadas com droga = 21948\n",
            "Nº de culturas que foram tratadas com controle = 1866\n",
            "\n",
            "\n",
            "com_droga       0.921643\n",
            "com_controle    0.078357\n",
            "Name: tratamento, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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": 0
        },
        "id": "-nI0RVNrLuf1",
        "outputId": "8d134f0c-fedf-4e4b-d071-06c211fd94e8"
      },
      "source": [
        "print('Nº de culturas que receberam dose', dados['dose'].unique()[0],'=', dados['dose'].value_counts().get('D1'))\n",
        "print('Nº de culturas que receberam dose', dados['dose'].unique()[1],'=', dados['dose'].value_counts().get('D2'))\n",
        "\n",
        "print('\\n')\n",
        "print(dados['dose'].value_counts(normalize=True))\n",
        "print('\\n')\n",
        "\n",
        "labels = dados['dose'].unique()[0], dados['dose'].unique()[1]\n",
        "plt.title('Culturas que receberam dose:')\n",
        "plt.pie(dados['dose'].value_counts(), labels=labels, radius=1, autopct='%1.1f%%',pctdistance=0.5)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de culturas que receberam dose D1 = 12147\n",
            "Nº de culturas que receberam dose D2 = 11667\n",
            "\n",
            "\n",
            "D1    0.510078\n",
            "D2    0.489922\n",
            "Name: dose, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAAD3CAYAAADmIkO7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAb7ElEQVR4nO3deXhb1ZnH8e/r3bEdJ44dSCAgINCyFBh2KNsEwua2wECGKUxJoZQy0JatQ9VpQ0UXKqYzQCnrMAtLngABulA0JWVa1gQKlAbKHghiS2jASeQ1tiWd+eNcE8XxHlvnSPf9PI8fW7LuPe+V9NO59+rec8UYg1LKPyWuC1BKDUzDqZSnNJxKeUrDqZSnNJxKeUrDqZSnCiacIpIUkaNd1xEGhfxci0hMRBa6rmM85DWcInK6iDwnIu0islpEfisih45hPkXzAig1mLyFU0QuAa4FrgS2ArYDbgROzFcNObWU5bvN8VYIy1AINXrNGDPhP0A90A7MG+IxtwE/yrl9JPB+zu0kcDRwHNAD9AbzfCH3/zmPjwELg78jgAG+ArwLPB7cfy/wIZACHgd2z5n+BOAVoA34APjWIHWXAv8GfAysBC4I2iobrq7g9kHAMmA98AJw5BDPURL4NvAi0A2UDTU90AD8D7AKWAf8Kud/nwOWB9MtA/bs1853guVfF8yjahTT9q8xCrwVPJevACfnPP7LwFLgmmB+K4FDgvvfA9YA84d4TnYAHgvm/TBwfb/n9wvAy8G8HwV2zfnft4PXtg14HTgquL8kp+YWYDHQkI+sbLJseQrncUC67w27JeEc6A0+inDeAdQA1cH9ZwN1QCW2V1+eM/1q4LDg76nAPoPUfR7wGjArCMMjjDCcwDbBi39C8IaYG9xuGiKcy4O2qoebHkgA9wT1lwNHBPf/TfCmPxD74TI/mHdlTjsv5SzT0r7XZoTTflJjcN88YGZQ42lABzAjJ5xp4Kxgfj/CfoDeELwux2DDUzvIc/IUcHXw2MODx/Y9v7sEbc0Nlv8y4E2gAvgUNvwzc94jOwV/Xwg8DWwbzPcW4K6cNl8ETi+WcJ4BfDjMY25j4sO54xDtTwkeUx/cfhf4GjB5mLr/AJyXc/sYRh7ObwN39pvfEgbpKYJ5nZ1ze9DpgRlAFpg6wHxuAn7Y777X2RjeZL9lOgF4axTTnj1Q/TmPXw6cGPz9ZWBFzv8+Ezx/W+Xc1wLsPcB8tsMGuybnvkU5z+8CYHHO/0qwPeWRwGzsh8zRQHm/+b5K0IsGt2dg19QG7Vwm4idf25wtQKMH2yDv9f0hIqUiEheRt0SkFfumAmgMfp+CfVO+IyKPicjBg8xzZu58gXdGUc/2wDwRWd/3AxyKfTMMuwzDTD8LWGuMWTdIu5f2m25WsCwDtfNOzv9GOy0icqaILM95/B5sfJ4B/przdxeAMab/fbUDLMdMYJ0xpqNfrbn//+S2MSYb1LaNMeZN4CLsh+UaEblbRHKX8Zc59b4KZLD7SvImX+F8Crv9cdIQj+kAJuXc3nqIxw50Ks1Ips+d7nTszqijsdvEkeB+ATDGPGuMORGYDvwKu90xkNXYN2ef7UZR13vYnm9Kzk+NMSY+SFv9l2Go6d8DGkRkygDzeA/4cb/pJhlj7sp5TP9lWjWKaT+pUUS2B24Fvg5MM8ZMwa4yyxDLOFKrgakiUtOv1j6rsEHrq0WC5foAwBizyBhzaPAYA1yVs4zH91vGKmPMB+NQ84jlJZzGmBRwOXCDiJwkIpNEpFxEjheRfw0ethw4QUQaRGRr7KfaYP4KREQkt/7lwD8E890POHWYsuqwHxgt2PBc2fcPEakQkTNEpN4Y0wu0YlcRB7IY+KaIbCsiU7E7EnINVddC4PMicmzQk1eJyJEisu0wtQ87vTFmNfBb4EYRmRq0f3gw3a3AeSJyoFg1ItIsInU5874gWKYG4LvYbdeRTpurBvvG/whARM7C9pxbzBjzDvAccEXwmh0KfD7nIYuBZhE5SkTKgUuxr/kyEfmUiMwRkUpgA7Z37nuNbwZ+HHywICJNIpL3bxXytv4crLufgX0yO7B7SRPAIcH/qrBvgFbsBvfFDL7NOQ14Ersn8fngvh2BP2L34CaA69h8m7MsZ361wK+xOxDeAc4MHjMbu8PgoWD+rcCzwKGDLFMZdk9jC/A2m++tHbSu4P8HYvc2rsW+gRPAdoO09clzMJLpsTtzbsd+mK0DfpEz3XHBcq3H9kD3AnU57fTtrV0fzGPSKKbtX+OPg/o+xu68eQw4x2zc5nwy57Gz7dtyk+nfH+L53xF4Inh+B9pbe3KwHKmg3d2D+/cEngle/7XAg2zcOVQCXILdlm7D7rW9MmeeLwNnTHReJGhMjRMRiWBDWm6MSbutRhWygjl8T6mw0XAq5SldrVXKU9pzKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwekZEMsGoAS+LyAsicmnfeasiMk1EHgmGFr3eda1qYrkeNkRtrssYszeAiEzHjokzGfg+9qTgBdiTlcflhGXlL+05PWaMWQOcC3xdRMQY02GMeRIbUlXkNJyeM8asxA4ZOd11LSq/NJxKeUrD6TkR2RE7LOMa17Wo/NJwekxEmrAjwV1v9Kz40NGREDwjIhngL9jLB6SBO4GrjR0QGRFJYvfeVmBHvzvGGPOKm2rVRNJwKuUpXa1VylMaTqU8peFUylMaTqU8pcfWei4STTRgrwfS97MD9vJ5tdiLMdViL6Rbjn09S7FX8GrFXh9lbfCzrt/vVcDLyXjzKpSXdG+tJyLRxEzgMGBfYCdsCHfAXtR3Iq3DXpjnpZzfLyXjzR9PcLtqGBpORyLRxKexYTw0+L2D24o281fgceB3wJJkvPm9YR6vxpmGM08i0cRW2GtzHo0NZOPQU3jnNYKgAo8m482djuspehrOCRRsL54C/ANwBHZ7sBj0AEux1ze9Kxlv1uN+J4CGc5xFoola4CRsII/B7qgpZmlsb3o78EAy3tztuJ6ioeEcJ5Fo4rPAN4AvYPeehlELNqQ3J+PNK1wXU+g0nFsgEk2UAfOAi4H9HZfjEwM8AlyXjDf/2nUxhUrDOQaRaKIKOAf4Z2A7x+X47nkglow3/8Z1IYVGwzkKwfbk+cAlwFaOyyk0zwLfT8abf+u6kEKh4RyBSDQhwFnAT9CxfLbUU9iQPuy6EN9pOIcRiSYOAH4OHOC6liLzJHBpMt78jOtCfKXhHEQkmpiO7SnPwh6rqsZfFvvB991kvLnDdTG+0XD2E+yBvQC4Aqh3XE5YvAOcl4w3P+S6EJ9oOHNEool9sd/T7e66lpBaBFyUjDd/5LoQH2g4+WSHz8XY1dgKx+WEXQtwSTLefIfrQlwLfTgj0UQTcBtwguNS1KYeBL6UjDevd12IK6EOZySamAMsBGa4rkUN6E3g5GS8+SXXhbgQynBGoolS7A6f76BDtfiuA/hKMt58j+tC8i104QzOq7wf+KzrWtSo/Dvw7WS8OeO6kHwJVTgj0cTO2NObfBt1QI3MI8BpYdmbG5pwRqKJ/YEE0OS6FrVF3gNOTMab/+y6kIkWiu2tSDRxHPZTV4NZ+GYBj0aiicNcFzLRij6ckWjiS8ADQI3rWtS4mQwsiUQTx7suZCIVdTgj0cRl2CN+in2okDCqBn4diSb+3nUhE6Votzkj0cRVwGWu61ATLgOcnow3L3ZdyHgryp4zEk0sQIMZFqXAomLsQYuu54xEE98Efua6DpV3aeCLyXjzfa4LGS9FFc5INDEf+B/0/Muw6gbmJOPNy1wXMh6KJpyRaOIY7PeYenGmcFsDHJiMNyddF7KliiKckWhib+x1Pepc16K88DJwSDLe3Oq6kC1R8DuEItHELGyPqcFUfXYH7g5OcChYBR3OSDRRDtwLzHRdi/LO8cA1rovYEgUdTuBK4EDXRShvfSMSTZzvuoixKthtzkg0cQL2bHndM6uGksbuwX3CdSGjVZDhjEQT2wDLKbxrXCo33gb2Ssab21wXMhoFt1obbOQvQoOpRm4H4GrXRYxWwYUTuBw43HURquCcE4kmml0XMRoFtVobiSb+Fvg/CvNDRbn3IbBHMt7c4rqQkSiYo2ki0UQlcCshCOb7N51NSUU1lJQgJaXMmH8tHa89SerJRfS2vMfWZ15N5YydB5y2a+WfWPv7/4Bsltq9jqH+oHkAfPSbn9L70TtU77Q/U4+YD8D6ZXdT0bg9k3Y5OG/L5tjWwE1AQRwkX0hv9MuAnVwXkS9bffFKZp71c2bMvxaAisbtaTr5X6icNfhg9CabYe3DNzF93hXMPOdGOl55jJ6P36VnzduUlFUy8+zr6Vm9gmx3B+n2tfSsej1MwewzLxJNfNF1ESNREOGMRBPbY4exDK3yxlmUT9t2yMf0rH6DsikzKJ+yNVJaTs2uh9O14mmkpIxsuhtjsphsGqSE1BMLqT/0jDxV750bggtVea0gwok90qPadRF5I8KaxZez+rYLaVs+8mv7pNtaKJu8cZik0rpGMu0tlDfOorS6ntW3Xcik2QeQXrcaYwyVW8+eiOoLwVQg5rqI4Xi/zRmJJo4FTnZdRz5tfcZVlNU1kulYz1/v+R7l07alatYeWzTPhqPP/eTvNfddQcOxXye17B561rxNVWRv6vY+bkvLLjRfjUQT1ybjzW+4LmQwXveckWiiArjOdR35VlZnv8ItrZnCpF0OpnvVyN4/ZXXTSLduHNI10/YxpbXTNnlM54qnqdh6NqZ3A73rV9N0UpTO15eS7d0wfgtQGMqAuOsihuJ1OIFLgF1cF5FP2Z4NZLs7P/l7w9t/pqJp+xFNWzFjF9LrVtG7/kNMppeOVx+nevbGQ49NJk3rc79m8oGnYNLdfHLko8lCJj3ei1IITo5EE96O/O/tam0kmmgEvue6jnzLdK7no1/8yN7IZqnZ7Qiqd9yXzjeWsfbhW8h0pVhz3xVUTN+BrU77Iem2Floeuo6t5l2BlJTSMPc81iy+HEyW2s/M3STYbc8nqN3jKErKqyhv2gGT7mbVf11A9U77UVJV62iJnfspcIjrIgbi7UEIkWjiB8AC13WoUDg1GW++33UR/XkZzkg0UYu9FHmD61pUKKwAdkvGm71at/d1m/NcNJgqf3YGznRdRH/ehTPYQ3uJ6zpU6FzsuoD+vAsncAawjesiVOjsEYkm5rouIpdX4YxEE4KO1K7c8ar39CqcwEnAp10XoULruOACy17wLZwXui5AhZoAX3NdRB9vvkqJRBMRYCU6YJdyqwXYJhlv7nZdiE8955loMJV704BTXRcB/oVTKR982XUB4MlqbSSaOAh4ynUdSgV6gaZkvDnlsghfes6CGNNFhUY54HykPufhDL7b9GIdX6kcJ7ouwHk4sdc6meW6CKX6OT44lNQZH8IZqiFIVMGoA+a4LMCHcP6t6wKUGoTTVVune2uD8zbX4fGIDCrUVgHbJuPNTkLiuuc8GA2m8tdMYB9XjbsOp16QSPnuIFcNaziVGtp+rhp2Fs7gwkQHuGpfqREKXzixwaxy2L5SI7FrJJqY5KJhl+E8zGHbSo1UKbC3i4ZdhnNPh20rNRpOVm1dhjM019pUBS904Qzt9edUwXESTidHCEWiiQbscBBKFYI0UJmMN2fz2airnlNXaVUhKQPyfiVsV+HUVVpVaGbmu0HtOZUaGQ2nUp6ake8GXYUz4qhdpcYqND3nZEftKjVWoQlntaN2lRqr0ITTyYHESm2B0Gxzas+pCk3eOxTtOZUambwPp6M9p1IjU/zhDAbqLc13u0ptobyH08XId9prDqOEbKae9tap0t42jdbOadLa1STru5tkfbqR1sw0SdEgbTKFjrJa6SyfRHdVJb2TyknXCDgdpbxYZZFWO4pr/rgIZwh6TWPq6GybKu1tDbS1N8r6DU2S6m4k1dsoqUyjtGanSWtJPe2lddJVXsOGyip6JpWTriklWwfUijAVmOp6SZRVgmnNd5suwtnhoM1Rm8SGzim0pxqktcP2XKkNjaR6psv6zDRJmQbamCrtpZPpLKuRrqoqeqoqSNeUkqkTmCzCZPRgi2KSzneDrs7n7MFeZm3CVNDbPYW21gZpb58mrZ2NpLqaZH1Pk6QyjZLKNtBqGqStdDKdZbXSVVlNd1UF6UllZOoEM1lkYutTBec1Yqld89mgq9HWO4ApQz2glEy6no7WqdLWPo3W9iZJbWiUVE+TrO9tJJVplFaZKm1ST0dZrXRVTGJDVSW9k8rI1JbYcFUBTcGPUluqM98NOgnngrI7HpspLVPsTo320jobrspKeqrLydSWkp0sQg3QEPwo5dqH+W7QSTi/UvZQBNjLRdtKjVHew+nqIISPHLWr1FitzneDrsL5saN2lRqr0IRzjaN2lRqr0IRzpaN2lRqr0ITzVUftKjVWoQnnK47aVWosDKHZWxtLvQ+0OWlbqdF7k1hqQ74bdXmtFF21VYXieReNajiVGt6fXDTqMpy63akKhfacSnkqdOF81mHbSo1Uklgqv0MgBNyFM5b6EF21Vf5z0muC254T4PeO21dqOE52BoGGU6nh/MFVw67D+SiQcVyDUoNZAzzjqnG34YylUjhcbVBqGL8llsq6atx1zwnwf64LUGoQD7ps3Idw6nan8lEPsMRlAT6E80lgvesilOrncWIppydnuA9nLNUDLHZdhlL9OF2lBR/Cad3uugCl+tFwAhBLLQPedF2GUoFlxFJvuS7Cj3Bad7ouQKnAza4LAP/Cmf8Ltyi1qRbgXtdFgE/hjKXexu65Vcql210MSTIQf8Jp6Y4h5dotrgvo41s470ZHg1fuPEIs9YbrIvr4Fc5YqgO41nUZKrS82BHUx69wWtcDKddFqNBZBfzSdRG5/AunPVPlBtdlqND5MbFUr+sicvkXTusaHFxJWIXW28Ctrovoz89wxlIfA//hugwfZLKGv7mlnc8tsp9Vf3g7zT63tLPHje3M/1UX6ezAXw3fvryHnX/ezs4/b+f25T0AdKcNxy3sYI8b27nx2Z5PHnvub7p4fnWoz3m/wrdeE3wNp/Vv2NN2Qu1nf+xh10b7MmWNYf6vurj71GpeOr+W7euF25dv/p5a22W44rFu/nhODc+cU8MVj3Wzrsuw5K00h25Xxov/VMOdL9rpXvgwQyYL+8wozetyeeQ1YKHrIgbibzhjqQ/wcFUjn95vzZJYkeacfSoAaOk0VJTCLtNskObuWMb9r6Y3m27Jm2nm7lhGQ7UwtVqYu2MZD72ZprwEOnsNvRkwQYe74JFufjinMm/L5KHLiaW8XG3wN5zW5YT4e8+LHtrAvx5dRYnY242ThHQWnltl30v3vZLmvdbNR9H4oC3LrPqNL+22k0v4oC3L3J3KSK7PctB/dfDNAyt44PVe9plRwsw6398GE+bPwH2uixiM369KLLUWiLouw4UH3+hleo2w78yNq5siwt2nVHPxkg0ccGs7dZVQKiOfZ1mJsOiUSfz5a7XM262Ma5/u4dKDK7lkyQZOXdzJA697t9k10b5HLOXt8dxlrgsYgf8GvgIc7LqQfFr6boYHXk/zvyva2JCG1m7DP/6ii4V/V80TZ9mX7XdvpXmjZfOec5u6Eh5Nblzdfb81y5GRTV/qG5/t4cy9ynn6/Qz1lcI9p1Yz545OvvCp8oldMH/8hljqf10XMRS/e04g+GQ7n5ANofmTo6t4/5I6khfVcfep1czZoYyFf1fNmg4bxu604aql3Zy3X8Vm0x47u4zfrUyzrsuwrsvwu5Vpjp29MZzrugwPrkhz5l7ldPYaSgREoKvX205kvKWAf3JdxHD8DydALLUcuMl1GT746dIedr2hnT1v7uDzu5QxZwcbuudWZTjngS4AGqqFBYdXsv+t7ex/azuXH15JQ/XG9d8fPNbNdw+rpESEY2eX8cS7aT5zUwdf2nPzoBepy4Idjl4TYwrk0zJWXw+8DmzluhRV0B4BjvJ5W7NPYfSc0HdY3yWuy1AFrRP4aiEEEwopnACx1CJ0OBM1dpf7MDbQSBVWOK3zsau3So3GMxTY6YiFs82ZK1a/J/BHoMp1KaogrAP2I5Za6bqQ0SjEnhNiqReBi12XoQpCFji90IIJhRpOgFjqZnSkeDW8BcRSD7kuYiwKN5zWV4GC2cBXeXc/sdSVrosYq8IOZyzVCpwKOL3gjPLSK8CXXRexJQo7nNB39NDJ6LmfaqMUcBKxVLvrQrZE4YcTIJb6PfAl7Ma/Crc0dgfQCteFbKniCCdALLUYuNB1GcopA5zl+9kmI1U84QSIpa4HCnYHgNpi3yCW8nLIkbEozIMQhhOr/0/sOaAqPP6FWOonrosYT8XVc270NeAXrotQebOg2IIJxRpOO2DTaehB8mGwgFjqR66LmAjFGU6AWCoNzAdudF2KmjDfKdZgQrFuc/YXq/8BsMB1GWrcdANnB6cQFq1whBMgVv9V7FAnoR09uUh8hD3AYJnrQiZaeMIJEKs/AXuwfI3rUtSYvAJ8LrgKetEr3m3Ogdgvpw8F3nRdihq1h4FDwhJMCFs4oe9Y3H2wV9FWheEW4IRgHKnQCNdqbX+x+nOBn6EjKviqFXvUzx2uC3Eh3OGEviFPFgOfcl2K2sRjwHxiqXdcF+JK+FZr+7NDnuyLHrDgi27gn4E5YQ4maM+5qVj9F4Grga1dlxJSfwH+MfjADD3tOXPFUncBnwauI2TXZnEsA/wU2F+DuZH2nIOJ1e+NPfQvVFc3c+Ah4FJiqVdcF+IbDedQYvUCnAVcBTQ6rqbYvAR8i1hqietCfKXhHIlYfQPwA+AcINTXaB8Ha7BXLP9PXy/37gsN52jE6mcC38KeLzrJcTWFpgv7nfKVxFI6WuIIaDjHIlbfiB1x/gKg3nE1vvsQuAG4mVjqY9fFFBIN55aw1wz9OnARuk3a3wvANcBdxFI6bOkYaDjHQ6y+BjvywnzgMECGnqBoGeBB4BpiqUdcF1PoNJzjLVYfwY6heyYw220xefMycC+wqBjGi/WFhnMixeoPwYb0NGCK42rG20vYQN5LLPWq62KKkYYzH2L1FcBngaOAo4H9KLwRGQzwInA/NpCvOa6n6Gk4XbA7ko7ABvUoYDe3BQ2oE3gWWAosA54illrrtqRw0XD6IFY/A3sC+G7Arjm/J+epgnbgbeA1bBCXAsuJpXrz1L4agIbTZ7H6bdkY1u2AqQP8TAHqBplDNzZ47djLJK7GhjD3Z6V+/+gnDWcxiNWXYQOaxV5ly/7o4XEFTcOplKf0fE6lPKXhVMpTGk6lPKXhLHIikhGR5SLysoi8ICKXikhJ8L+5IvInEflL8HuO63rVRmWuC1ATrssYszeAiEwHFmG/P/0+8DHweWPMKhHZA1gCbOOsUrUJ3Vtb5ESk3RhTm3N7R+yRP40m58UXEQFagBnGmO78V6r609XakDHGrMQe1zu9379OAZ7XYPpDV2sVIrI7dhCzY1zXojbSnjNkgtXaDHagLURkW+CXwJnGmLdc1qY2peEMERFpAm4GrjfGGBGZAiSAqDFmqdvqVH+6Q6jIiUgGe5mDcuwxt3cCVxtjsiLyPeA7QO7oBccYY9bkv1LVn4ZTKU/paq1SntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSntJwKuUpDadSnvp/LRHNHGI62SAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gnayMeZ3MyZC",
        "outputId": "a7898ec1-0bb6-428a-f238-9c482698357c"
      },
      "source": [
        "print('Nº de culturas que foram expostos a droga por', dados['tempo'].unique()[0],'h =', dados['tempo'].value_counts().get(24))\n",
        "print('Nº de culturas que foram expostos a droga por', dados['tempo'].unique()[1],'h =', dados['tempo'].value_counts().get(72))\n",
        "print('Nº de culturas que foram expostos a droga por', dados['tempo'].unique()[2],'h =', dados['tempo'].value_counts().get(48))\n",
        "\n",
        "print('\\n')\n",
        "print(dados['tempo'].value_counts(normalize=True))\n",
        "print('\\n')\n",
        "\n",
        "labels = '24h', '72h', '48h'\n",
        "plt.title('Janelas de tempo:')\n",
        "plt.pie(dados['tempo'].value_counts(), labels=labels, radius=1, autopct='%1.1f%%',pctdistance=0.5)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de culturas que foram expostos a droga por 24 h = 7772\n",
            "Nº de culturas que foram expostos a droga por 72 h = 7792\n",
            "Nº de culturas que foram expostos a droga por 48 h = 8250\n",
            "\n",
            "\n",
            "48    0.346435\n",
            "72    0.327202\n",
            "24    0.326363\n",
            "Name: tempo, dtype: float64\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO0AAAD3CAYAAADxANNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2dd3xb1d3/318Nb1tO7Dg7URYJFEMYYYcyHqYDlJayWwMP/MpoH3YxFILYppTxsFNGQ6Gs0hB4cNmEUUYChBCFmeVs4iROnDieks7vjysT4XjGks690nm/XnpZuveeez6S9dE599xzvl9RSmEwGJyDS7cAg8HQO4xpDQaHYUxrMDgMY1qDwWEY0xoMDsOY1mBwGMa0mhCRd0Xk3ATXcZaI/CeRdRiSjzFtByTDUHbHfAb2xZjWYHAYxrRdICL9ROQVEVknIhujz4fF7H9XRG4SkQ9FZIuIvCEixTH79xORj0Rkk4h8KSKHdFLPGBF5R0Q2iMh6EfmHiBTG7L9KRFZF6/hORA7v5DxFIvKyiGwWkTnAmHb7J4jImyJSGz3PyZ2c5xZgMnC/iNSLyP3dlReR6SLyoIi8Gi3zoYgMEpF7op/dtyKyR8zx1SJytYh8Hd3/NxHJitl/nogsitb1sogM6ez/lHYopcyj3QN4FzgXKAJ+BeQA+cA/gZntjlsM7ARkR19XRvcNBTYAx2L9OB4RfT0gto7o87HR/ZnAAOB94J7ovvHACmBI9LUfGNOJ7meB54FcYFdgFfCf6L7c6HnOBjzAHsB6YJeuPoOY112WB6ZHX+8FZAHvAEuB3wJu4GZgVsz5qoEFwHCgP/AhcHN032HRc+0Z/UzuA96PKfsKUKH7e6Lt+6lbgB0f7b+wMdsnAhvbHXdtzOsLgdeiz68CnmxX/nWgvKs6ovt+AXwRfT4WqAH+C/B2odkNtAITYrbdGmPaU4AP2pWZBlzfk8+gu/JR0z4Ss+8PwDcxr0uBTTGvq4HzY14fCyyOPn8M+HPMvrzoe/Pr/m7Y4WG6x10gIjkiMk1ElonIZqwWsFBE3DGH/RDzvAHrCwYwEvh1tGu8SUQ2AQcBgzuoZ6CIPBvtAm8GngKKAZRSi4BLgABQEz2uo67iAKwWcEXMtmUxz0cC+7bTcwYwqIcfR0/Kr4153tjB6zx+Snutbe9rSKx2pVQ9Vi9laA+1pjTGtF1zOVb3dF+lVAFwcHS79KDsCqyWtjDmkauUquzg2FsBBZRG6zkztg6l1NNKqYOwjKOA2zs4xzoghNXdbGNEOz3vtdOTp5S6oBP97Zd/9bZ8T2ivdXX0+Wqs9wqAiORiXaqs6kNdKYMxbdfkY7UQm0SkP3B9L8o+BRwnIkeJiFtEskTkkNiBrHb11AN1IjIUuLJth4iMF5HDRCQTaIrqibQ/gVIqDMwAAtEewi5AecwhrwA7ichvRMQbfUwSkZ070b8WGN2H8j3hIhEZFv1s/wQ8F93+DHC2iEyMvu9bgdlKqeo+1JUyGNN2jgLuwRpgWg98ArzW48JKrQBOAK7BagVXYJmxo8/8BqxBlzqgCst8bWQClVENPwAlwNWdVPt7rC7oD1jXmH+L0bMFOBI4Fasl+wGrxc7s5Fz/C5wUHdm9dwfK94SngTeAJVgDejdHtb4FXAf8C1iDNQp+aluh6Aj1NX2o19FI9ELfEIOIzAVuVErN1K0lVRGRaqyBrrd0a3EapqVth4j8DNgZ+EK3FoOhI4xpYxCR27G6a1cppZZ1d7zBoAPTPTYYHIZpaQ0Gh2FMazA4DGNag8FhGNMaDA7DmNZgcBjGtAaDwzCmNRgchjGtweAwjGkNBodhTGswOAxjWoPBYRjTGgwOw5jWYHAYxrQGg8MwpjUYHIYxrcHgMIxpDQaHYUxrMDgMY1qDwWEY0xoMDsOY1mBwGMa0BtsiIsNFZFY0h+1XInJxu/2Xi4hqywksIgERuUKP2uTh0S3AYOiCEHC5UmquiOQDn4vIm0qpr0VkOFaakuV6JSYfY1oH4q+ocmNlnBuFlSRrJFZqzCKsBM0FWDl9crCy70ViHgpoAWqx0kduwMoTtAErb85iYGF1ZdmG5L2jjlFKrcHShFJqi4h8g5Xu8mvgbuCPwEvtiu0iIu9iZeG7Ryl1b/IUJwdjWpvjr6gaCeyNlWF9D6ys88MBb4Lr3QgsAhZiZWyfA3xaXVm2OZH1doaI+LHe/2wROQFYpZT6UmS7rKMTgEOxMhF+JyIPKaVak6k10RjT2ohoC7ofcASwP5ZRizTJ6QdMij7aiPgrqr7FMvBs4O3qyrKFiRYiInlYGfQuweoyX4PVNe6IKqVUM9AsIjXAQGBlojUmE5MWRDP+iqrRwFFYX8JDAZ9eRb1mMfBq9DGrurKsMZ4nFxEvVm7c15VSd4lIKfA20BA9ZBhW6s19gPOBeqXUX6JlFwBTUi2vrTGtBvwVVaOw8q2eBpRqlhNPmoB3sJJCv1hdWba1LycTq+/7BFCrlLqkk2Oqgb2VUutFJEAamNZ0j5OEv6JqEHAKllH31SwnUWQBx0Yf9f6KqpnAk1jd6PAOnO9A4DdAUETmRbddo5T6d1zUOhTT0iYYf0XVQcAfgBNJ8OCRjVmDlZX+gerKstW6xTgdY9oE4K+oygJOxzLrRM1y7EQr8E/g7urKss90i3EqxrRxxF9RlQ9cjDXKqWvU1yl8CNwJzKyuLDNfwl5gTBsH/BVVuVit6hUYs/aWecD11ZVlL+sW4hSMafuAv6IqG7gIa2bOAM1ynM5s4KrqyrL3dAuxO8a0O4i/ouqXwF1YUwgN8ePfwCXJmLThVIxpe4m/omo8cC+dz8gx9J1moBK4rbqyrFm3GLthTNtDotet1wGXAhma5aQLi4CLqivL3tAtxE4Y0/YAf0XVIcDjWKtqDMnnOeD31ZVl63ULsQNmEXxXBHxZZ11zSwBrap4xrD5OAeb7K6rMJQmmpe2cgG8X4NlW5S7YvfmR4gaycnVLMqCAe4Cr0/la17S0HRHw/Q74DCj1Snjk0xm3zNUtyQBYC/ovBWb7K6p20S1GF6aljSXgKwQeA37ZftflLed/+q/IwZO2L2TQxFbgrOrKshd0C0k2xrRtBHzDgdeBnTvaHVZSM6n5QXctPjPjyT4o4CYgkE5TIU33GCDg+xnwMZ0YFsAtqmRmxvXmhr+9EGAq8K/oLbm0wJg24DsI+AArYFiXjHDV7HeRe+Z/Ei/K0EtOBD6OxtNKedK7exzwnQA8i7V4u0coxeaft9y9ebkaOCxxwgw7yErgiOrKsm91C0kk6dvSBnznYQUL67FhAUQomJkxdT2k86+dbRkGvO+vqNpDt5BEkp6mDfimAn8F3DtSvL9smXiz5/H34yvKECcGALP8FVUH6haSKNKrexzwuYD7gQv6eiqlaJ7ScsuKr9SosX0XZkgADcCJqThvOX1MG/AJ8BRWGJi40KAyv9u9+ZFRrXjMAgJ70ggcWV1ZllKDh+nUPb6FOBoWIEeaxz/kvfujeJ7TEFeygVf8FVUpFacrPUwb8J0DXJ2IUx/u+mLywa4v5yfi3Ia44ANe91dUjdMtJF6kfvc44DsMeI0Ehi9tVe7lE5v/2n8r2XmJqsPQZ5YBB1ZXlq3SLaSvpHZLG/DtjHVbJ6Hxhr0SHvFMxs1fJLIOQ58ZCfw7FWZOpa5pA74BQBVQmIzqdnMtnfxr97tzklGXYYfZDZiuW0RfSc3uccCXhbVwff9kVhtWsm5S84Mus6jA9lxbXVl2i24RO0rqtbTWrZ3pJNmwAG5RA2ZmTDWLCuzPTf6Kqim6RewoqWdauBErPIkWRrjW7fc/7hkpdV8wBRHgH9HImo4jtbrHAd/PgVlY/xRtmEUFjmEusF91ZZmjMsWnTksb8GUDj6LZsGAtKngp47oNQiSiW4uhS/YEbtAtorekjmnhZsA284D7Sf3ut3oe+0C3DkO3XBVNR+oYUqN7HPDtC3yEzX6ElKL5hJabls9XY1JmNk6KUg3sXl1Ztlm3kJ5gqy/5DhHwZWAFErfdexEh87mMm5SXUItuLYYu8WOlenEEtvui7wBTAduG08yWlp2mee8yiwrsT7m/ouow3SJ6grNNG/BNBK7SLaM7DnXNO/gQ1zyzqMD+POSvqMrULaI7nGvagM+D1S326JbSHSK4HvHe2S+Xxi26tRi6ZCfgSt0iusO5prUSOTsmFpBXwsOfzbhpnm4dhm652l9RNVy3iK6wfSvVIVZg8am6ZfSWUlf15JPds+Y8Hz50H91aAFSohR+evgoVaoVIhJzxB1I4+Ywf99e+NY36+W8y4rKOg/i31Cxlw+v3o5obQYTB5XcDQs2MmwhvWU/+HmXk71kGwIbX7iNv4jFkDrLNXbnOyAHuAE7VLaQznNrSXgvY/tqjIyo9j44qZpM9Uja6vQw89VaGnHM/g8++l8aln9O8yoo+2rxmIZGm+k6LqkiY9a/cSdFRFzHk3AcZePpt4HLTuHQumcN2YfA591P/1TsAtNQsQUUiTjBsGyfbOaKj80wb8I0CztYtY0dxiRowM3PqYt06AEQEV0Y2ACoSgkgYRFCRMBvffZzCQzr/mJuWziVjgJ+MktEAuLMLEJcbcblRrc0QDltJO4BNHzxF4eQzE/5+4ohgpRuxJc4zrZWNPaGL2hPNMFm/7yWeF2wxW0pFwqz+2x9Yed+ZZPknkjlkPFvmvkLO2H3x5PXvtFxr7WoQYe1z17Fm+sXUzba60Fmj9iBUV8OaJy+nYO/jaFg4m4yBY/DkO261Ypm/omo/3SI6wlmmDfjGAb/VLSMeXOyeMdEva1bo1iEuN0POvo9hF06nec33NK1YQMO3H5K/13FdllORME0rv6b4uCsYeMbtNHz/MY3V8xCXmwHHX8mQs+8lZ/xBbP7sJQomnUjt24+w7sVbaVg4O0nvLC7crFtARzjLtHANOxhg3G6IkD8zY2qtXRYVuLLyyBqxG03L5tO6aTWrpp3HyofOQbU2s2raedsd78kvImv4z3Dn+HB5s8gevTcta3/a69/yRRV5ux5G8+rvcGXmUnzCVWz+9MVkvaV4cLi/ouoQ3SLa4xzTBnxDiHMIVN0UytbdKz2PaMtUEG6o+3GwKdLaTFP1F2QMGsvw3z/FsAseZ9gFjyPeTIb+7pHtymaN3ovWdcuItDahImGaVyzAWzxi27mb6mlc9Cm5ux6GCjWDiHW9HHJcAvc/6hbQHifd8rkYSLmg4Ce73zvg6fDh33+pxu6U7LrD9bWsr7obVARUhJwJk8kZ2/ndqIaFs2n5YSGFk8/EnZVH/qRf8MMTl4FA9ui9yRmzLed23YfP4DvgZERcZI/aky1zq1jz2O/J2+OYZLy1eHK0v6JqfHVl2Xe6hbThjFU+AV8+sAIrhm3K0agyFu7e/MiIFryOvI2VBjxQXVn2e90i2nBK9/h3pKhhAbKlZdwj3js/1q3D0Cnl/ooq23z/nGLai3QLSDQHu+YffJhr7pe6dRg6JA84R7eINuxvWmuBu1+3jEQjgmua9+4is6jAtvQ502K8sL9p4WTdApKFV8LDns+40bS29mScv6LKFnPG7W1aK4bxSbplJJOfuZYddJr7bUfNQEgjbHHL0d6mhX2BEd0elWLc4nl8TAkb1+nWYdiOU/wVVdon99jdtGnTNY7FJar4xcypS3XrMGzHIEB7SBr7mjYNu8axDJUN+1zmed5kKrAf2rvI9jUt7AfYOoJAovmDe+bEUbJ6uW4dhp8wxV9RpdU3djbtr3UL0I0IeTMzpm6yy6ICAwDFwF46BdjTtGneNY7FJw273eH9q7ZFBYYOOVpn5fY0reka/4Rfud4/YKIsss2EdQNH6azcrqY9RLcAOyFCxrMZN7kyaHXcurYUZT9/RVWhrsrtato9dQuwG1nSOu4x718+0a3DAFiBGLTd+jGmdRAHuYKTD3d9bmIn24N9dVVsP9MGfIXAaN0y7Eh0UUFxHg2OyO6W4kzq/pDEYD/Tmla2SzwSGfbPjBtNXiD97OWvqNKSwNyY1oHs7Fp+0JnuN831rV4KgAk6KrajabXeuHYKN3qmjxtIbY1uHWmOli6yHU1rWtoe4BJVNDNz6jLdOtIcLalD7GVaK4DbON0ynMJgqZ10pec5W2QqSFO0JCeyl2mtXy4tF/dO5UL3S3uOltWmxdXDGB2V2tG0hl4gQu6LGVM3u4iEdWtJQ0bpGEG2m2mH6RbgRHzSUPoX78Omm5x8stDwnbWbabXN53Q6J7r+c+Ce8v23unWkIUnvItvNtP10C3AqInifzrjFk0lLk24taUbSV6MZ06YQWdI69nHvHSaSY3LpPIlvgjCmTTEOcH118JGuT7/QrSONSPp31pg2xRBBHvT+b0k+W+t0a0kT0r6lNQNRccAjkaEvZNywQLeONCGNW9qAz0UKZ8ZLNuNdKw/8rft1k4kv8aR1S+vDzIaKKwHP38ebRQUJJzfZFdrJtOZ6Ns64RPV/KfM6M8UxsSQ9TYgn2RV2QdKvZ5tCioP/tpXmMIQicNLOHm44NIszZjTw2eoIXhfsM9TNtClZeN0/7QTMWhri0te33RL9dn2EZ0/K5hcTvJwxo4Hg2ghTdvJw6+FZANz8fjO7lrj4xQRvUt/jINk46SrPMx/cHjptclIrTh/S2rRJb/Uz3fBOeS55GUJrWHHQ37ZyzLgQZ5R6eepE66M5fUYjj85t5YJJGT8pe+goD/POzwOgtlEx9t4tHDnGw/y1YbI9wvwL8jjiya3UNSkaWhWzV4W59uDMZL9FAJqKPwjnFX5Zq6XylMddD2VJrdFOpm1IdoUiQl7Ui60RaA1bF9XHjtvWGu4zxM3KzV0H+H/h61aOGechxyt4XdAYUkSUojUMbhdMndXMDYdoMqxI4/TC/AkiyR8wSQ/CGd0fE1+6bd1EZLyIzIt5bBaRS0TkDhH5VkTmi8iLIlIYPf4sEbl/B7Rs3YEyfSYcUUx8uJ6SO7ZwxGgP+w7b9jvWGlY8Ob+Vo8d2/dv27IJWTtvVMvrOA9wMyHGx57StHLeTh0W1ESIK9hysJ0NiZf9+cyIig7RUnh60JrvCbltapdR3wEQAEXEDq4AXgfHA1UqpkIjcDlwNXNUHLUlvaQHcLmHe+XlsalKc+FwDC2rC7FpiGezCqiYOHulh8sjOP6Y1WyIEayIcNWbbMfccnfXj8+OeaWDalCxueb+ZL9eGOWK0h/P2Ss6Pc73Ilhn5uaVJqSx9aUl2hb29jjwcWKyUWqaUekMpFYpu/4SfLlEaIiKvichCEflzD8+tpaVtozBLONTv4bVF1lu64d1m1jUo7jqq627t81+1cuIEz3YDVQAvfdvKXoNd1LcoFm+M8Pyvc3jhm1YaWlVC3kN7biju/7kSMd3ixJL0lra3pj0VeKaD7ecAr8a8ngicApQCp4hIT1ZCNAJJzQ63bmuETU2WgRpbFW8uCTGh2MWjc1t4fXGIZ36VjUu6vnX8zILQj13jWFrDintmt/DHAzNpbN12AzocgZYkLFff5HJtfC03x8TbSjxJT9XSY9OKSAZwPPDPdtv/BISAf8RsflspVaeUagK+BkZ2W0GgTgFJHeFcU6849Imt7PZQPZMe2coRoz1M2cnL+a80sXZrhP0f28rEh+u58T3r//LZ6jDnvtz4Y/nqTRFWbI7wc//216sPfNpC+e5ecrzCbgNdNIQUpQ/Vs9dgN4VZiZ9DcvWAovmIFCS8IsOqZFfYm9HjY4C5Sqm1bRtE5CxgCnC4Uiq2zxf76xPuRT01WPk/k8JuA9188bu87baHpnb8Xd97iJtHj8/+8bW/0MWqy/I7PPaS/bZ1q0WEZ36V00e1PafG7a75T3aWtgj4acbSZFfYm+7xacR0jUXkaOCPwPFKqXgNIpkpd3HgypKibxBJ3q9EemNP04pILnAEMCNm8/1APvBm9FbQw3HQY0zbR1Z4PCvnZmbur1tHGpF00/ao26qU2goUtdvWYcxXpdR0YHrM6ym90LOuF8caOuDykuKliJgAecmjOtkV2mnBAGj41UolFnm9S7/J8B6gW0caURMsDyb9VqXdTGvCpPSBSwYWr8GaAGNIDloaGWPaFGF+ZsZ3yzwecy2bXIxpCdRtRMM1QipweUlxHdLNTBBDvFmio1J7mdZirm4BTmN2VuZXP3g8++jWkYaYljaK6SL3kitLipM+ad0AmJb2R0xL2wveysn+YqPbbRKXJZ8Q8KmOiu1o2s91C3AS1w0oSm78GkMbc4LlwS06KrafaQN1a4E1umU4gZl5uXPqXa5ddetIU97WVbH9TGthrmu7QYG6paifiROtj7d0VWxX05rr2m54qiD/4yaXa7xuHWlKA1bgBy3Y1bTv6hZgZ8IQvrt/4WDdOtKYD4LlQW0j9nY27XrdIuzKXwsLPm4VGaVbRxqj7XoW7GraQF0YK3icoR0t0DKt0OfXrSPNMabthH92f0j6cU//wk/CZumdTmqBeToF2Nm0s4ANukXYiUaRhn8U5E/QrSPNmRUsDyY1AGF77GvaQF0I00X+CbcV9ZsTESnRrSPNebX7QxKLfU1rYbrIUbaIbJ6Zl7u7bh1pTj3wvG4RdjftO5guMgDXDyiaq0RMOlC9PK1r6mIs9jat1UWeqVuGbja6XLVv5mTvpVuHgXgEL+wz9jatxQu6BeimYkBREJGOAywbksWnwfKgLabXOsG0b5PGXeS1bvfaj7KzzAJ3/diilQV75aftmEBdKwHffUBAtxQdXFFS/B0iByeyjkhLhKW3LUWFFCqsKJhUwMATB7Li4RU0VjcibiF7dDZDy4cinu0j2rRsaGHV46sI1YZAYOSlI8kYkMGKh1fQtLKJ/In5DDrJyrZZ83INWUOzKNjLURlL6oBndYtow/6mtbgXuBwrOHrasNzjWTkvMyPhwdrEK/iv8uPOcqNCiiW3LiG/NJ/C/QsZ9jtrHsfKh1dS+34tRYcVbVd+5V9XUnJcCXm75hFuCiMiNK1owpXhYtzN41h6x1LCDWEiLREaFzdScrzj7lo9GSwPaknF2hFO6B63BXx7SLeMZHNpSfEyRBK+yF1EcGdZkVdV2GptEcjfPR8RQcRqaUO1oe3KNq1qggjk7WrlRHJnuXFlusBtteAqolAhBS6omVFDyYmOMyzANN0CYnFKSwtwF/A/QFZ3B6YC33m9S77P8O6XrPpURLH4+sW01LTQ//D+5IzZlgpIhRSbPtrE4NO3X1jU8kML7hw3y+9bTsu6FvJ2yWPgyQPJGpKFJ9/D4usXU3hAIS1rW1BKke3P3u4cNufDYHlwgW4RsTijpYW2iBaP6paRLC4bWLw2mYHHxSWMvWks4+8aT+OSRppWNv24b/XfV5O7Uy6543O3K6ciiq3fb2XQKYMYc/0YWta1sPGDjQAMPmMwY28aS/ExxdTMqGHgLwdS83INyx9YTu27Sc1q2hdsMwDVhnNMa/FnNGTeTjZfZmZ8t9zjSVorG4s7103uzrnUB+sBqJlZQ2hLiEGnDerweG8/L1kjssgoyUDcQv6e+TQta/rJMZvnbibLn0WkOULLuhZGXDSCzZ9tJtKsdQpvT/geGw1AteEs0wbqVgBP6paRaC4rKd6czMDjoc0hwlut9PSRlgj1X9WTMTiD2vdq2bJgC8MvGI64OpaTPTqbSEOE0GbrenfrN1vJHLItN68KKTa8sYEBxw4g0rLNpD9e69qba4Plwe0v5DXjpGvaNm4DyoGUzFnzYXZWsMbjSWpC6FBdiJWPrERFFCjw7eOjYGIBC85ZgLfIy5KbrPC+BXsXUHJCCY1LG6mdVcvQc4YiLmHQKYNY+mcrbnf2yGz6HbJttuWGtzdQeGAhrkwXWcOzUC2KhdcuJH+3fNy5tv4XfoZNJ/bITxO4O4SA72msJNcpx+QRQ+dtcrsn6tZh4IhgeVBb8LaucFb3eBu3Ara/IOotb+RkzzWGtQVv2dWw4FTTBuoWYLN7Z/Fg6oCizO6PMiSYEHCJbhFd4UzTWlwN/KBbRLz4V17unK0u18906zDwQLA8+JVuEV3hXNMG6uqAS3XLiAcRiNxW1M+sldVPDXC9bhHd4VzTAgTqngXe0C2jr/y9IP+TZpdrnG4dBiqC5cE63SK6w9mmtbgQK+K7IwlB6N7+hUN06zDwITBdt4ie4HzTBuoWA1fplrGjPFzo+7hVxK9bR5pTC5weLA864v6n801r8QCaA0jvCC3Q/GhhwWjdOgycHSwPLtctoqekhmkDdQo4B9isW0pvuLN/v0/CIkN160hz7g6WB1/WLaI3pIZpAQJ1y4GLdcvoKY0iDc8U5O2iW0eaMwcHXlqljmkBAnXTcciCgpuL+s1RIgN060hjNgGnBMuDjls1llqmtTgX+EC3iK7Y7JK6/8vLNdMV9XJ2sDxYrVvEjpB6pg3UtQAnAot0S+mMqcVF85RIoW4dacy9wfKgY+Npp55pAQJ1G4AyYKNuKe3Z4HKtf9sEHtfJZ8CVukX0hdQ0LUCg7nvgl9gs0kVFSfFXiOTp1pGm1GBdx2rL4h4PUte0AIG6d4H/p1tGG2vc7jWfZGXuq1tHmrIB+K9geXCJbiF9JbVNC20jyrfplgFwRUnxQkTSIpqkzagDjgyWB4O6hcSD1DetxZ/QnDaz2uNZPj8JgccN21EPHB0sD87VLSRepIdprRlTvwU+0iXh0oHFK5IReNzwExqAsmB58BPdQuJJepgWIFDXBBwJvJbsqr/J8C5e5PWaVja5NAEnBMuD7+sWEm/Sx7QAgbqtwPEkedbUZSXF6xBJr89aLy3ASXaO89QX0u+LFKhrxQrBemcyqvs8M/OblR6PGTFOHiHg1GB5sEq3kEThzBCq8SLguwIra0HCAoMfNnzIZ+s8nr0TdX7DT2gEyoPlQa2Djokm/VraWAJ1fwHOwvp1jjsfZGfNN4ZNGsuAg1LdsJDupgUI1P0dOIEEhKy5ekBRGndjkso7wN6pdFunK4xpAQJ1/wYOx5o1Exdezc35vM7t3j1e5zN0yt1YEyfW6xaSLNL7mrY9Ad9Y4CmgzwNH+44c9nWDy2UWuSeORuDcYHnwad1Cko1paWMJ1C0CDsSKfbvD17nP5+d9YgybUKqBA9LRsGBa2s4J+CZhtbo79aZYBO0RZQ4AAAPaSURBVCL7jBy2pNnlGpsYYWnP21grdeJ2KeM0TEvbGYG6T4E9gAd7U2y6L/9jY9iE0ArcDByVzoYF09L2jIDvaOBxYHBXh4UgNMk/fFVIZGRyhKUNc7CuX1NilU5fMS1tTwjUvQaUAjO6OuyBfr6PjWHjSj1WhM39jWG3YVra3hLw/Qa4AxgYu7kFmif5h9dGRLpsjQ09ZiZwsZOCiCcLY9odIeDLw4ozdDmQC3BLUb/3ni3I/7lWXanBAuCSYHnQcRkjkoUxbV8I+AYBNzSInLLfyGEtJo5xn9gATAWmBcuDYd1i7IwxbRyYev+ocS/m5wWAUzHjBL1lLdYI/X3B8qDtomfaEWPaOFL6ROl4rNA2pwNuzXLsThBrCuLTwfJgc28Li4gbKxzqKqXUFBE5HGuswYU1gHWWUmqRiEwHXlFKvRA/6Xoxpk0ApU+UjgWuxmp5czTLsRMKeBW4q6/XrCJyGbA3UBA17ffACUqpb0TkQmAfpdRZqWha05VLAMHy4KJgefC/gUFY2fzexfrCpiuNwDRgl2B5sCwOhh2GFYz+0ZjNCiiIPvcBq2P2HSwiH4nIEhE5qS912wHT0iaJ0idKRwBnYgWYG69ZTrJYgjUp5eF4zmISkRewwuLmA1dEW9rJWLeJGrFSnu6nlNocbWlzgVOACcDLSilHz1gzptVA6ROl+2CZ91SgSLOceBLGinj5CvB/wfLgN/GuQESmAMcqpS4UkUPYZtoZwO1KqdkiciUwXil1btS0byql/hEtv0UplR9vXcnEmFYjpU+UerG6eb/AWl3kxBZgI1aEy1eAVxM9AiwitwG/wVqFlYXVJZ4FTFBKjYkeMwJ4TSm1S/trWhGpV0o5Oi2LMa2NKH2idABwQMxjb6wvpp1QwLdAFZZRPwyWBxMSrqc72lparB+9H4ADlFLfi8h/Y7XGv0pF03p0CzBsI1geXAe8FH20tcR7ss3EB9LNooU4EgYWA98AX0cf3wDfBsuDW5OkoUcopUIich7wLxGJYLX+52iWlTBMS+swSp8oLcAy7qDo39jnsdv603mUybbBmthHDZYp20z6vdOzy6UqxrQpSukTpRlYo6bh9o9geTCiU5uhbxjTGgwOw0yuMBgchjGtweAwjGkNBodhTGswOAxjWoPBYRjTGgwOw5jWYHAYxrQGg8MwpjUYHIYxrcHgMIxpDQaHYUxrMDgMY1qDwWEY0xoMDsOY1mBwGMa0BoPDMKY1GByGMa3B4DCMaQ0Gh2FMazA4DGNag8FhGNMaDA7DmNZgcBjGtAaDwzCmNRgchjGtweAwjGkNBofx/wG6sLiwJeELRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z359PrtwwfDn"
      },
      "source": [
        "# Manipulando a coluna *droga* e investigando alguns compostos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-qFWOPzwmJU"
      },
      "source": [
        "mapa = {'droga':'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "uU-_oBIGbi3v",
        "outputId": "7f873de5-c81c-4bac-bf12-cfa4b5b082cd"
      },
      "source": [
        "sns.set()\n",
        "plt.figure(figsize=(10, 8))\n",
        "\n",
        "compostos_head = dados['composto'].value_counts()[0:5]\n",
        "\n",
        "ax = sns.countplot(x = 'composto', data=dados.query('composto in @compostos_head.index'), order=compostos_head.index)\n",
        "ax.set_title('5 Compostos mais utilizados')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAH1CAYAAABycxNoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3hNZ/7//1d22KlzJA5N6FRrSlNGhY1SYQQtcZwYI1XaqZoeBlM6qFYrbVAVimqVdvho+xlT35mqRlAUVUxbpaiqdtqJU0kQiUMictz37w8/69NUTpQktzwf1+W67HXfa633WmvvtV/7Xmtn+xhjjAAAAGAdV1kXAAAAgCtDkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAMqJFStWaNiwYaW+nqZNm+rQoUOSpEmTJmnevHlXdX3btm1Tp06druoyAVzgw9+RAyqmoUOHavfu3apUqZIkqV69elq7dm2h/Q8cOKDZs2dr27Ztys3NVXBwsCIjI/XAAw/I19e3tMq+YuHh4ZoyZYo6dOhQ1qWUqiNHjqhr16765ptvnGP9c02bNtW6det08803X5Matm3bpnHjxmnz5s3XZPlARVbwqxpAhTBp0iQNHDiw2H6HDx/WH/7wB0VGRio+Pl716tXT/v37NW/ePJ07d041a9YshWoBAD/HpVUAxZo7d65CQ0P19NNPq169epKkW2+9VS+//LIT4jZs2KBevXrJ4/Fo6NChSkhIcOYPDw/XwoUL1adPH7Vs2VLPPPOMTp48qeHDhys0NFR//OMfdebMGUkXRpCaNm2q//f//p86duyojh07atGiRc6ysrOzNXXqVKdt6tSpys7OliSlpqbq0UcflcfjUdu2bTV48GB5vV6NGzdOiYmJeuyxxxQaGqq//e1vxdb85ptvKiwsTKGhobr33nv12WefFbhvJkyYoOeff97ZlqioKCUnJ2vq1Klq06aNevTooX379uVbbrdu3RQaGqqIiAh99NFHTtv777+v++67T5JkjNGLL76o9u3bq1WrVurTp4++//77AmsIDw/Xp59+6jx+9dVXNXbsWEnSkCFDJElt2rRRaGiodu3alW89BW3P7NmzJcnZXxf/3X777Xr//fclSVOmTFHnzp3VqlUrRUZGaseOHc4yMjMzNWHCBLVp00YRERH6+uuv860jISFBQ4cOlcfjUa9evbRhwwan7ZNPPlFERIRCQ0MVFhaW79gDKIABUCENGTLEtGvXzrRt29YMGjTIfP7554X27dChg3nvvfcKbd+/f7+58847zdatW012drZ58803Tbdu3UxWVpYxxpguXbqYgQMHmuTkZHPs2DFz1113mf79+5tvvvnGZGZmmqFDh5pXX33VGGPMjz/+aJo0aWLGjBljzp07Z7777jvTrl078+9//9sYY8ycOXPMwIEDzcmTJ01KSooZNGiQmT17tjHGmJkzZ5rnnnvOZGdnm+zsbLN9+3bj9XqdGi4uo7iaExISTKdOncyxY8ecmg4dOlTgtj/11FOmbdu25uuvv3a2pUuXLmb58uUmNzfXzJo1ywwZMsTpv3r1anPs2DGTl5dnVq1aZe68805z/PhxY4wxy5YtM1FRUcYYYzZv3mx+97vfmTNnzhiv12v++9//Ov1+7ufbNnfuXPPXv/413/7Myclx2n+6HmOMadKkiTl48KCzPbNmzbpkHZs2bTJ33323SUxMNMYY88EHH5jU1FSTk5NjFi1aZDp06GAyMzONMcbMmDHD3HfffebUqVMmMTHR9OrVy4SFhRljjMnOzjbdunUz8+fPN1lZWebTTz81LVu2NAkJCcYYY+6++26zfft2Y4wxp0+fNnv37i1wmwFcwIgcUEGNHTtW69ev15YtWzRo0CA99thjOnz4cIF9T58+rbp16xa6rNWrV6tz5866++67VblyZT388MPKzMzUrl27nD5DhgxRnTp1VL9+fXk8HrVo0UJ33HGH/Pz81L1793yjVpI0YsQIVa1aVU2bNlVkZKRWrlwpSYqPj9eIESMUGBiogIAAjRgxQitWrJAkVapUScnJyUpMTFTlypXl8Xjk4+Nz2TX7+voqOztbCQkJysnJUcOGDfWrX/2q0O3v3r27mjdv7myLn5+f+vfvL19fX0VEROjbb791+vbs2VP169eXy+VSRESEbr75Zu3Zs+eSZVaqVEnnzp3T/v37ZYxR48aNndHQ0nbgwAFNmDBBc+bMUVBQkCSpX79+ql27tipVqqRhw4YpOztbBw4ckCR9+OGHeuyxx+Tv76+goCANHTrUWdZXX32ljIwMPfLII3K73Wrfvr26dOmiVatWSbqw3f/973+Vnp6uWrVqqVmzZqW/wYBFCHJABXXnnXeqevXqcrvd+t3vfqdWrVrpk08+KbCvv7+/kpOTC13WiRMnFBwc7Dx2uVwKCgrS8ePHnWl16tRx/u/n55fv8Q033KCMjIx8y7wYGCSpQYMGOnHiRIHrCg4Odtoefvhh3XzzzRo2bJi6du2qN99884pqvvnmm/XMM8/o1VdfVYcOHTRmzJh82/JzgYGB+balqG374IMP1K9fP3k8Hnk8Hv3www86derUJcts37697r//fsXExKh9+/Z67rnnlJ6eXmgN10paWpr+/Oc/a/To0fJ4PM70RYsWqWfPnmrdurU8Ho/S0tKc7Thx4kS+4/fT/XzixAndeOONcrlc+dov7t+5c+fqk08+UZcuXTRkyJB8HwYAXIogB0CS5OPjI1PIl9jbt2+vdevWFTpvvXr1lJiY6Dw2xigpKUn169e/4nqSkpKc/ycmJjqjUT9fV1JSktNWvXp1TZgwQRs2bND8+fO1ePHiQu9tK67mPn366N1339XHH38sHx8fzZw584q35aKjR4/q2Wef1XPPPadt27Zpx44duu222wrt/8ADD+j999/X6tWrdfDgQS1cuLDAflWqVNH58+edxz8N3YWNSJaE1+vVX//6V7Vr106DBg1ypu/YsUMLFy7UnDlztH37du3YsUM1atRwnj9169bNd/x++v969erp2LFj8nq9+dov7vcWLVpo/vz5+vTTT9WtWzeNHj36iusHKgKCHFABnT17Vlu2bFFWVpZyc3O1YsUK7dixQ2FhYQX2/8tf/qJdu3Zp+vTpTkg4dOiQxo4dq7Nnz6pnz5765JNP9NlnnyknJ0f/8z//I7fbrdDQ0Cuu8fXXX9f58+f1ww8/6P3331dERIQkqVevXpo/f75SU1OVmpqqefPmqU+fPpKkjz/+WIcOHZIxRjVq1JCvr68TZOrUqaMff/zRWX5RNe/fv1+fffaZsrOz5Xa75efnl28E6UqdP39ePj4+CggIkCQtW7ZMP/zwQ4F99+zZo6+++ko5OTmqUqWK3G53oTXcfvvtWr16tXJycvT111/n+zMyAQEBcrlc+ba9pGbPnq3z589r4sSJ+aafO3dOvr6+CggIUG5url577bV8o4U9e/bUm2++qTNnzujYsWP63//9X6etRYsWuuGGG7Rw4ULl5ORo27Zt2rhxoyIiIpSdna0VK1YoLS1NlStXVrVq1a7KfgeuZ/z5EaACys3N1Zw5c7R//375+vrq1ltv1bx583TLLbcU2P9Xv/qVli5dqjlz5qh3797Kzc1VgwYNFBkZqWrVqqlmzZqaMWOGJk+erOPHjyskJEQLFiyQ2+2+4hrbtm2r7t27yxijYcOGqWPHjpKkP//5zzp37pz69u0rSerRo4f+/Oc/S7oQLidPnqzU1FTVrFlT9913n+666y5J0iOPPKIpU6ZoxowZevzxx/Xwww8XWnN2drZefvllJSQkqHLlygoNDVVMTMwVb8tFv/71rzVs2DBFRUXJx8dH/fv3V6tWrQrse+7cOb344os6cuSI3G63OnbsqIcffrjAvqNHj9aTTz6ptm3bqk2bNurTp49Onz4t6cJo3WOPPab77rtPubm5hY7qFWTVqlVKTk5W27ZtnWkvvPCCevXqpbCwMN17772qWrWqHnzwwXyXUkeOHKno6Gh17dpV9erVU2RkpN555x1Jktvt1oIFC/TCCy/ojTfeUP369RUbG6vGjRsrOztbcXFxmjx5svLy8nTLLbdoxowZJa4XqIj4g8AAypWS/AFbAMAFjFkDAABYiiAHAABgKS6tAgAAWIoROQAAAEsR5AAAACxVob8SdurUOXm9XFkGAADll8vlo9q1qxXYVqGDnNdrCHIAAMBaXFoFAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFKVyroAG9SoeYNu8Ktc1mVc1zKzcpR2NrOsywAAwCoEuRK4wa+yBo9fUtZlXNf+EXu/0kSQAwDgcnBpFQAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUpVKa0XTp0/X2rVrdfToUcXHx6tJkyY6cuSIRowY4fRJS0tTenq6vvjiC0lSeHi43G63/Pz8JEljx45VWFiYJGn37t2aNGmSsrKy1KBBA82YMUOBgYGltTkAAABlrtSCXNeuXfXAAw/o/vvvd6Y1bNhQcXFxzuOpU6cqLy8v33xz585VkyZN8k3zer0aN26cpk2bJo/Ho9dff10zZ87UtGnTru1GAAAAlCOldmnV4/EoKCio0Pbs7GzFx8drwIABxS5r79698vPzk8fjkSRFRUVpzZo1V61WAAAAG5TaiFxxNm7cqPr166tZs2b5po8dO1bGGLVu3VpPPvmkatasqaSkJAUHBzt9AgIC5PV6dfr0afn7+5d4nYGB1a9a/fjl6tatUdYlAABglXIT5JYtW3bJaNySJUsUFBSk7OxsTZ06VTExMZo5c+ZVW2dKSrq8XlNsPwJG6UhOTivrEgAAKHdcLp9CB5/KxbdWjx8/ru3bt6tPnz75pl+8FOt2uzV48GDt3LnTmZ6YmOj0S01NlcvluqzROAAAANuViyC3fPlyde7cWbVr13amZWRkKC3twgiNMUarV69WSEiIJKl58+bKzMzUjh07JElLly5Vjx49Sr9wAACAMlRql1anTJmidevW6eTJk3rooYfk7++vVatWSboQ5CZOnJivf0pKikaNGqW8vDx5vV41btxY0dHRkiSXy6XY2FhFR0fn+/MjAAAAFYmPMab4m8SuU5dzj9zg8UtKoaKK6x+x93OPHAAABSj398gBAADg8hHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsVWpBbvr06QoPD1fTpk31/fffO9PDw8PVo0cP9evXT/369dOWLVuctt27d6tv37669957NWzYMKWkpJSoDQAAoCIotSDXtWtXLVmyRA0aNLikbe7cuYqLi1NcXJzCwsIkSV6vV+PGjdOkSZO0du1aeTwezZw5s9g2AACAiqLUgpzH41FQUFCJ++/du1d+fn7yeDySpKioKK1Zs6bYNgAAgIqiUlkXIEljx46VMUatW7fWk08+qZo1ayopKUnBwcFOn4CAAHm9Xp0+fbrINn9//xKvNzCw+lXdDvwydevWKOsSAACwSpkHuSVLligoKEjZ2dmaOnWqYmJiSu0yaUpKurxeU2w/AkbpSE5OK+sSAAAod1wun0IHn8r8W6sXL7e63W4NHjxYO3fudKYnJiY6/VJTU+VyueTv719kGwAAQEVRpkEuIyNDaWkXRmGMMVq9erVCQkIkSc2bN1dmZqZ27NghSVq6dKl69OhRbBsAAEBFUWqXVqdMmaJ169bp5MmTeuihh+Tv768FCxZo1KhRysvLk9frVePGjRUdHS1Jcrlcio2NVXR0tLKystSgQQPNmDGj2DYAAICKwscYU/xNYtepy7lHbvD4JaVQUcX1j9j7uUcOAIAClOt75AAAAHBlCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiqUmmtaPr06Vq7dq2OHj2q+Ph4NWnSRKdOndL48eN1+PBhud1u3XzzzYqJiVFAQIAkqWnTpmrSpIlcrgt5MzY2Vk2bNpUkbdy4UbGxscrLy1OzZs00bdo0ValSpbQ2BwAAoMyV2ohc165dtWTJEjVo0MCZ5uPjo+HDh2vt2rWKj4/XTTfdpJkzZ+abb+nSpYqLi1NcXJwT4s6dO6fnnntOCxYs0EcffaRq1app0aJFpbUpAAAA5UKpBTmPx6OgoKB80/z9/dWuXTvnccuWLZWYmFjssjZv3qzmzZurUaNGkqSoqCh9+OGHV7VeAACA8q7ULq0Wx+v16t1331V4eHi+6UOHDlVeXp46deqkUaNGye12KykpScHBwU6f4OBgJSUlXfY6AwOr/+K6cfXUrVujrEsAAMAq5SbITZ48WVWrVtWQIUOcaZs2bVJQUJDS09M1btw4zZs3T2PGjLlq60xJSZfXa4rtR8AoHcnJaWVdAgAA5Y7L5VPo4FO5+Nbq9OnTdejQIc2ZM8f5YoMk51Js9erVNXDgQO3cudOZ/tNLsImJiZdctgUAALjelXmQmzVrlvbu3at58+bJ7XY708+cOaPMzExJUm5urtauXauQkBBJUlhYmL7++msdPHhQ0oUvRPTs2bPUawcAAChLpXZpdcqUKVq3bp1Onjyphx56SP7+/pozZ47eeOMNNWrUSFFRUZKkhg0bat68edq/f78mTZokHx8f5ebmKjQ0VE888YSkCyN0MTExevTRR+X1ehUSEqKJEyeW1qYAAACUCz7GmOJvErtOXc49coPHLymFiiquf8Tezz1yAAAUoNzfIwcAAIDLR5ADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAAS5U4yC1atKjA6YsXL75qxQAAAKDkShzk5s2bV+D0+fPnX7ViAAAAUHKViuvw2WefSZK8Xq8+//xzGWOctiNHjqhatWrXrjoAAAAUqtggN3HiRElSVlaWnnnmGWe6j4+P6tatq2efffbaVQcAAIBCFRvkNm7cKEkaP368YmNjr3lBAAAAKJlig9xFPw1xXq83X5vLxZdfAQAASluJE9g333yjQYMGqWXLlmrWrJmaNWumO+64Q82aNSt23unTpys8PFxNmzbV999/70w/cOCABg0apHvvvVeDBg3SwYMHf3EbAABARVHiIDdhwgS1a9dOy5Yt0/r167V+/Xpt2LBB69evL3berl27asmSJWrQoEG+6dHR0Ro8eLDWrl2rwYMHa9KkSb+4DQAAoKIocZA7evSoxowZo8aNG6tBgwb5/hXH4/EoKCgo37SUlBTt27dPvXv3liT17t1b+/btU2pq6hW3AQAAVCQlvkeue/fu2rp1q8LCwq7KipOSklS/fn35+vpKknx9fVWvXj0lJSXJGHNFbQEBAZdVQ2Bg9auyLbg66tatUdYlAABglRIHuaysLI0cOVKtW7dWnTp18rXZ+m3WlJR0eb2m2H4EjNKRnJxW1iUAAFDuuFw+hQ4+lTjI/frXv9avf/3rq1ZUUFCQjh8/rry8PPn6+iovL08nTpxQUFCQjDFX1AYAAFCRlDjIjRw58qquODAwUCEhIVq5cqX69eunlStXKiQkxLk8eqVtAAAAFYWP+elvbhXh4k91FaR9+/ZFzjtlyhStW7dOJ0+eVO3ateXv769Vq1YpISFBEyZM0NmzZ1WzZk1Nnz5dt956qyRdcdvluJxLq4PHL7ns5aPk/hF7P5dWAQAoQFGXVksc5MLDw/M9PnXqlHJyclS/fn1t2LDhl1dZBghy5QdBDgCAgl2Ve+Qu/lTXRXl5eZo/f76qVav2y6oDAADAFbni39by9fXVY489poULF17NegAAAFBCv+hHUv/973/Lx8fnatUCAACAy1DiS6udO3fOF9rOnz+v7OxsRUdHX5PCAAAAULQSB7kZM2bke1ylShXdcsstql6dX0cAAAAoCyUOcm3btpUkeb1enTx5UnXq1JHL9YuuzAIAAOAXKHESS09P1/jx49WiRQt16tRJLVq00FNPPaW0NP5kBAAAQFkocZCbMmWKzp8/r/j4eO3Zs0fx8fE6f/68pkyZci3rAwAAQCFKfGl1y5YtWr9+vapUqSJJuuWWWzRt2jR17979mhUHAACAwpV4RM7Pz0+pqan5pp06dUput/uqFwUAAIDilXhE7ve//72GDRumP/7xjwoODlZiYqLeeustDRw48FrWBwAAgEKUOMg9/vjjql+/vuLj43XixAnVq1dPw4cPJ8gBAACUkRJfWp06dapuueUWvfXWW1q9erXeeustNW7cWFOnTr2W9QEAAKAQJQ5yK1euVPPmzfNNa968uVauXHnViwIAAEDxShzkfHx85PV6803Ly8u7ZBoAAABKR4mDnMfj0SuvvOIEN6/Xq1dffVUej+eaFQcAAIDClfjLDhMnTtSjjz6qjh07Kjg4WElJSapbt64WLFhwLesDAABAIUoc5G688UYtX75ce/bsUVJSkoKCgtSiRQt+bxUAAKCMlDjISZLL5VLLli3VsmXLa1UPAAAASojhNAAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSlcq6gCNHjmjEiBHO47S0NKWnp+uLL75QeHi43G63/Pz8JEljx45VWFiYJGn37t2aNGmSsrKy1KBBA82YMUOBgYFlsg0AAABlocyDXMOGDRUXF+c8njp1qvLy8pzHc+fOVZMmTfLN4/V6NW7cOE2bNk0ej0evv/66Zs6cqWnTppVa3QAAAGWtXF1azc7OVnx8vAYMGFBkv71798rPz08ej0eSFBUVpTVr1pRGiQAAAOVGmY/I/dTGjRtVv359NWvWzJk2duxYGWPUunVrPfnkk6pZs6aSkpIUHBzs9AkICJDX69Xp06fl7+9f4vUFBla/qvXjl6lbt0ZZlwAAgFXKVZBbtmxZvtG4JUuWKCgoSNnZ2Zo6dapiYmI0c+bMq7a+lJR0eb2m2H4EjNKRnJxW1iUAAFDuuFw+hQ4+lZtLq8ePH9f27dvVp08fZ1pQUJAkye12a/Dgwdq5c6czPTEx0emXmpoql8t1WaNxAAAAtis3QW758uXq3LmzateuLUnKyMhQWtqFERpjjFavXq2QkBBJUvPmzZWZmakdO3ZIkpYuXaoePXqUTeEAAABlpNxcWl2+fLkmTpzoPE5JSdGoUaOUl5cnr9erxo0bKzo6WpLkcrkUGxur6OjofH9+BAAAoCLxMcYUf5PYdepy7pEbPH5JKVRUcf0j9n7ukQMAoABW3CMHAACAy1NuLq0C10LtWm5VcvuVdRnXvdzsLJ06k13WZQBAhUOQw3WtkttPX8YOL+syrnutxy+URJADgNLGpVUAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgAAwFIEOQAAAEsR5AAAACxFkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsFSlsi5AksLDw+V2u+Xn5ydJGjt2rMLCwrR7925NmjRJWVlZatCggWbMmKHAwEBJKrINAACgIig3I3Jz585VXFyc4uLiFBYWJovygyQAABzVSURBVK/Xq3HjxmnSpElau3atPB6PZs6cKUlFtgEAAFQU5SbI/dzevXvl5+cnj8cjSYqKitKaNWuKbQMAAKgoysWlVenC5VRjjFq3bq0nn3xSSUlJCg4OdtoDAgLk9Xp1+vTpItv8/f1LvM7AwOpXdRvwy9StW6OsS8AvwPEDgNJXLoLckiVLFBQUpOzsbE2dOlUxMTHq3r37NV9vSkq6vF5TbD/eoEpHcnLaVV8mx670XIvjBwCQXC6fQgefysWl1aCgIEmS2+3W4MGDtXPnTgUFBSkxMdHpk5qaKpfLJX9//yLbAAAAKooyD3IZGRlKS7vwSd4Yo9WrVyskJETNmzdXZmamduzYIUlaunSpevToIUlFtgEAAFQUZX5pNSUlRaNGjVJeXp68Xq8aN26s6OhouVwuxcbGKjo6Ot+fGJFUZBsAAEBFUeZB7qabbtIHH3xQYFurVq0UHx9/2W0AAAAVQZlfWgUAAMCVIcgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGCpSmVdwKlTpzR+/HgdPnxYbrdbN998s2JiYhQQEKCmTZuqSZMmcrku5M3Y2Fg1bdpUkrRx40bFxsYqLy9PzZo107Rp01SlSpWy3BQAAIBSVeYjcj4+Pho+fLjWrl2r+Ph43XTTTZo5c6bTvnTpUsXFxSkuLs4JcefOndNzzz2nBQsW6KOPPlK1atW0aNGistoEAACAMlHmQc7f31/t2rVzHrds2VKJiYlFzrN582Y1b95cjRo1kiRFRUXpww8/vJZlAgAAlDtlfmn1p7xer959912Fh4c704YOHaq8vDx16tRJo0aNktvtVlJSkoKDg50+wcHBSkpKuuz1BQZWvyp14+qoW7dGWZeAX4DjBwClr1wFucmTJ6tq1aoaMmSIJGnTpk0KCgpSenq6xo0bp3nz5mnMmDFXbX0pKenyek2x/XiDKh3JyWlXfZkcu9JzLY5fzVp+8nO7r/py8X+ysrN19kxWWZcBoAgul0+hg0/lJshNnz5dhw4d0oIFC5wvNwQFBUmSqlevroEDB2rx4sXO9G3btjnzJiYmOn0BXD/83G79cfETZV3Gde2th16RRJADbFXm98hJ0qxZs7R3717NmzdP7v//0/eZM2eUmZkpScrNzdXatWsVEhIiSQoLC9PXX3+tgwcPSrrwhYiePXuWSe0AAABlpcxH5H744Qe98cYbatSokaKioiRJDRs21PDhwzVp0iT5+PgoNzdXoaGheuKJC5/Mq1evrpiYGD366KPyer0KCQnRxIkTy3IzAAAASl2ZB7nbbrtN//nPfwpsi4+PL3S+bt26qVu3bteqLAAAgHKvXFxaBQAAwOUjyAEAAFiKIAcAAGApghwAAIClyvzLDgCA65N/Dbcq3+BX1mVc13Iys3Q6LfuqL7dWzSpy+xERrrXsrFydOXv+Fy2DowQAuCYq3+Cn1Q88VNZlXNci3lksXYMg5/arpBcnvnfVl4v8npn6+1+8DC6tAgAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJYiyAEAAFiKIAcAAGApghwAAIClCHIAAACWIsgBAABYiiAHAABgKYIcAACApQhyAAAAliLIAQAAWIogBwAAYCmCHAAAgKUIcgAAAJayOsgdOHBAgwYN0r333qtBgwbp4MGDZV0SAABAqbE6yEVHR2vw4MFau3atBg8erEmTJpV1SQAAAKWmUlkXcKVSUlK0b98+LV68WJLUu3dvTZ48WampqQoICCjRMlwunxKvr07taldUJ0ruco7H5XDXDLwmy0V+1+r41alestczrty1OnaSVKUOr79r7Vodv1r+Va/JcpFfSY5fUX18jDHmahZUWvbu3aunnnpKq1atcqZFRERoxowZatasWRlWBgAAUDqsvrQKAABQkVkb5IKCgnT8+HHl5eVJkvLy8nTixAkFBQWVcWUAAAClw9ogFxgYqJCQEK1cuVKStHLlSoWEhJT4/jgAAADbWXuPnCQlJCRowoQJOnv2rGrWrKnp06fr1ltvLeuyAAAASoXVQQ4AAKAis/bSKgAAQEVHkAMAALAUQQ4AAMBSBDkAAABLEeQAAAAsZe1vrVZETZs21c6dO1WtWv7ffT116pTGjx+vw4cPy+126+abb1ZMTIwCAgJ05MgRDRgwQNu2bStwme+9957efvttuVwu+fr66plnnpHH45EknT59WjExMfrmm29UqVIl9ezZUyNHjpQk7d69W5MmTVJWVpYaNGigGTNmKDDw+vpNxY8//livvPKKjDEyxmjkyJG64447NGLECKdPWlqa0tPT9cUXXxS4jIvH7NSpU0XON336dK1du1ZHjx5VfHy8mjRpcsmyXnvtNb366qtOu9fr1X333afz589LkurWrasXXnhBDRs2lFT08Suq7Xq1adMmvfLKK8rNzVWtWrU0bdo03XTTTVq/fr1efvll+fn5adasWTp06NAlx/2ee+6RJB04cEATJkzQ6dOn5e/vr+nTp6tRo0aSdMlyZs6cqSNHjsjlcqlq1ap67rnnFBISUmDfMWPGOHX+9Llx5MiRy3q+XU8Kev3dc889hZ4Hi5rnSvfjmjVrNH/+fOfxsWPH1KZNG7322mtFznf+/Hk9++yz+s9//iNjjG677TZNmTJF1atX1+bNmzVz5kz5+PgoNzdX3bp10+jRo+Xjc+1+77YshIeHy+12y8/PT5I0duxYhYWFFXruOXDggCZNmqTk5GRVqlRJv/nNbxQdHa0bbrhBkrRgwQKtWLFClSpVUrVq1RQTE6Pbbrut0PUX9d5X3LnTOgbWaNKkiUlPT79k+qlTp8znn3/uPH7ppZfM008/bYwx5scffzRt27YtcHmpqakmNDTUJCcnG2OMWb9+venZs6fT/uijj5rFixc7j0+cOGGMMSYvL89069bNbN++3RhjzLx588yECRN+2caVM16v13g8HvOf//zHGGPMt99+a1q2bGny8vLy9ZsyZYp54YUXCl1OYcfs5/Nt377dJCYmmi5dujjr/Km9e/eahx9++JL2s2fPOv9/6623zIgRI5zHhR2/4tquR6dPnzZt27Y1+/fvN8YY88EHH5hhw4YZY4x5+OGHzerVq40xxR/3oUOHmg8++MBZxtChQ511/HQ5xuQ/Nh999JHp379/oX1/qqjnVHHPt+tFUcehsNdUSV+zxlz5fuzXr5/58MMPi+23ePFiM2rUKOP1eo3X6zWjRo0yb775pjHGmPT0dJObm2uMMSY7O9sMGDDArF+//rJrKe8KO5cVdu758ccfzTfffGOMufAe88QTT5jXXnvNGGPMvn37zG9/+1tz7tw5Y4wxb7/9thk+fHiR6y/qvc+Yos+dtuHSainYtWuX7rvvPvXt21d9+/bV1q1bNX36dA0YMEB9+/bVgw8+qKNHjzr9P/74Y0VGRqpv377q37+/vvvuO6dt0aJF6tevn+69916tXbtWkuTv76927do5fVq2bKnExMR8Nbz00kvq06eP+vTpox07dkiS86n13Llzki58Sr3xxhslSQcPHtT333+vBx980FlG3bp1JUl79+6Vn5+fM3IXFRWlNWvWXLX9VV64XC6lpaVJurBv6tWrJ5fr/14y2dnZio+P14ABA5xp69atU48ePdSvXz/NmzevwOUWNJ/H4yn05+Wys7MVExOj559//pK2GjVqOP9PT0936ivq+BXVdr06dOiQ6tSpo1tuuUWS1LlzZ23dulVPP/20vvzyS82cOVNDhw6VVPhxT0lJ0b59+9S7d29JUu/evbVv3z6lpqbqxRdfvGQ5Pz82F0dcCup7UUHPjZK0XY+Kev0VdB4sbp6LCtqPf/3rXxUZGak+ffpoxIgROnPmzCX1fPPNNzp27JjCw8MlScnJyRo6dKgiIyPVq1cvxcbGOn19fHyUmZmpnJwc5eTkKCMjwzm3VqtWTb6+vpKkrKws5eTkXFLj9aqoc0/Dhg11xx13SLpwHFu0aOG8j/n4+CgnJ0eZmZmS8r9XSReuLF18fx0wYIBOnjzptBX03icVfu6UpE8++URRUVGKjIzUoEGDtHv3bqdt+fLlGjhwoCIjI/XAAw9o//79V2Xf/CJlnSSvd6dOnTIdOnQwX375pTHGmNzcXHP69GmTkpLi9PnnP/9pRo8ebYwxZv/+/aZDhw7mwIEDxhhjsrKyTFpamjHmwujOq6++aowxJiEhwbRt29acPHky3/ry8vLMgw8+aN5++21jzIVPJU2aNDHLly83xhjz+eefm7CwMJOVlWWMMWbFihUmNDTUdO7c2YSFhZmDBw8aYy6MIERFRZmnn37a9O/f3wwfPtx8//33xhhj1qxZY/70pz/lW2+LFi3MqVOnrs5OKyc+/fRT07ZtW/Pb3/7WtGnTxuzatStf+4cffmj69u3rPE5OTjZt27Y1CQkJxhhj3nzzzQJHD34+308V9Ck2NjbW/P3vfy+0ffjw4aZDhw6mV69e5tixY8aYoo9fUW3Xq7Nnz5q2bduar776yhhjzDvvvGOaNGli9u7da4YMGWI2btzo9C3suH/99dcmIiIi33J79uxp9u7da4wxlyzHGGOeeeYZ07lzZ9OpU6d8+7igvsYU/dwoqu16VNhxKOo8WNxr1piC9+NPz8ezZs0yM2bMuGS+F154wUyZMsV5nJmZ6by2s7OzzdChQ80nn3zitI0ePdp4PB7j8Xic8/tFe/bsMb179za/+c1vzIsvvmi8Xu9l75/yrkuXLqZPnz6md+/eJjo62pw5c6bE557z58+biIiIfCOVCxcuNHfeeafp2LGjiYiIMKmpqcaYC+9p3bp1c0b20tPTTWZmZrHvfcYUfO48dOiQ+cMf/uC8737//femc+fOxpgLV07+9Kc/OcvYtGmTGTRo0FXec5evYnwMKEO7d+9W48aN1apVK0mSr6+vatWqpc2bN+sPf/iDevfurUWLFunbb7+VJH366afq1KmTc9+N2+1W9erVneUNHDhQknTrrbfqjjvuyPdJQZImT56sqlWrasiQIc60ypUrq2/fvpKkdu3a6YYbbtD+/fuVnp6uJUuW6L333tOmTZs0YcIEjRw5UsYYeb1effXVV4qMjHQ+gTz++OPXbD+VN7m5uXrjjTf0+uuv6+OPP9b8+fM1evRoZ/RSkpYtW5bvU/1XX32lO+64w/mZuEGDBhW47J/PV5Rdu3Zp7969Gjx4cKF9/va3v2nLli3q1auXcz9PUcevIh7bGjVqaPbs2Zo2bZoiIyOVkpKimjVrOiMjF5XkuF+OqVOnatOmTRozZky+EZvCFPXcuJznje2KOw4FnQdLeuwK2o9xcXHOiNzKlSud8/FF2dnZWrlyZb758vLyFBsbq759+yoyMlI//PCDc/Xk008/lSRt3bpVW7duVU5OjhYtWuTM+5vf/Ebx8fHatGmTvvnmm3wjRdeLJUuWaMWKFVq2bJmMMYqJiSnRuSc3N1djxozRXXfdpa5du0qSjh49qg0bNmjdunXasmWLfve732nChAmSLtz72q9fP2dkr1q1as59eYW9911U0Llzy5YtOnz4sO6//37169dPY8eOVW5urk6ePKmNGzfqu+++08CBA9WvXz+9/PLLOnbs2LXdkSVAkCsDR48e1bRp0/Tyyy9r5cqVevHFF5Wdnf2Llzt9+nQdOnRIc+bMKdFQ/datW1WjRg0neEREROjw4cM6deqUgoKCFBQU5Fw+veeee5ScnKzU1FQFBQXlu3Sbmpoql8slf3//X7wN5cW3336rEydOqHXr1pKk1q1bq0qVKkpISJAkHT9+XNu3b1efPn0ua7mXO9/27duVkJCgrl27Kjw8XMeOHdPDDz+srVu35uvncrn0+9//XnFxcZJU7PErrO161qFDB7377rt6//33NWTIEGVmZupXv/pVvj5FHfegoCAdP35ceXl5ki68kZ84caLQS+I/1b9/f23btk2nTp0qtE9Rz40rfb7ZqrjX35XOU9B+3LFjh959910tXLhQ8fHxGj169CXn448++kgNGzbU7bff7kxbvHixzp49q3/961+Kj49Xt27dlJWVJUlaunSpunfvLj8/P/n5+SkiIqLAm+4DAgLUqVOn6/LWlIuvC7fbrcGDB2vnzp3Fnnvy8vI0duxY1apVS88++6yzrDVr1qhJkyaqV6+epP97PV0NPz93SlJYWJji4uKcf1u3blWdOnVkjNGAAQOc6StWrNCmTZuuSh2/BEHuGmvZsqUSEhK0a9cuSReeqElJSapcubLq1q0rr9erpUuXOv3vvvtubd68WQcPHpR04ZNgenq6075s2TJJF+412Ldvn1q2bClJmjVrlvbu3at58+bJ7XbnqyEnJ0fx8fGSLpy0MjMzdeutt6phw4bat2+fUlJSJEmff/65qlevrtq1a6t58+aqWrWqfvjhB0kXAkWtWrWctszMTOdT5NKlS9WjR4+rvevK1I033qhjx445n94SEhKUkpLivPEvX75cnTt3Vu3atZ15WrZsqX379jnH7l//+tclyy1ovqI88sgj2rp1qzZu3KiNGzfqxhtv1KJFi9SxY0elpqbmC19r1qxR06ZNJanY41dY2/UsOTlZ0oURyVmzZikqKkpVq1bN16eo4x4YGKiQkBCtXLlSkrRy5UqFhIQoICDgknWdO3dOSUlJzuONGzeqVq1aRX7YKeq5cbnPG9sV9/or6DxY3DxSwfvx7Nmzql69uvz9/ZWdne0s+6cKGsVLS0tT3bp15efnp+PHj2vDhg1OW8OGDbV161bn6saWLVucb1geOHBAXq9XkpSRkaHNmzcX+C11m2VkZDj3KhpjtHr1aoWEhBR57vF6vZowYYJ8fX01derUfN/ibdiwob788ktlZGRIunAP28X9+dvf/lZxcXHOfXHnzp1zAnVh731FnTvvvvtubdmyxalRkvbs2SPpwjdx4+LinFG4vLw87d279yrvvcvHnx+5xvz9/fXqq6/qpZdeUkZGhlwul5566in16NFDERERql27tjp37uyEokaNGmny5MkaM2aM8vLy5Ovrq5deesl5kuXl5al///46f/68YmJiFBgYqB9++EFvvPGGGjVqpKioKEkXnvgXb7b39/fXd999p4ULF0q6EPrcbreaN2+u4cOHa8iQIapcubLcbrdeeeWVfDdlP/3008rOzlaVKlX02muvycfHRz4+PoqNjVV0dHS+Pz9yPalbt66ef/55PfHEE/n2x8U34uXLl2vixIn55gkMDNTkyZP12GOP6YYbbnD+ZMVPFTSfJE2ZMkXr1q3TyZMn9dBDD8nf31+rVq0qssbk5GQ9/fTTysnJkaR8x8HHx6fQ43dxWwpru17NmTNHO3fuVE5Oju6++26NHTv2kj7FHffnn39eEyZM0Ouvv66aNWtq+vTpBa7r/PnzeuKJJ3T+/Hm5XC7VqlVLCxYsKHIfF/bcKK7telTccSjoPCipyHmkgvdjWFiYVqxYoXvvvVe1a9eWx+PR119/7bQnJSVp586dmjNnTr75hg4dqieeeEK9e/dW/fr11b59e6dtxIgRmjRpkvPFmNtvv925hLhhwwYtX75cvr6+8nq96tatm3Op+HqRkpKiUaNGKS8vT16vV40bN1Z0dHSR56VPPvlEK1asUJMmTRQZGSlJatWqlaKjo3XPPfc4l2Tdbrdq1qypadOmSbpwyfSRRx7RQw89JB8fH7ndbi1YsEBS4e99RZ07GzVqpBkzZmjixInOF1ZatWqlFi1aqE2bNho9erQef/xx5eXlKScnRz169FDz5s1Lexfn42OMMWVaAQAAAK4Il1YBAAAsRZADAACwFEEOAADAUgQ5AAAASxHkAAAALEWQAwAAsBRBDgDKmSNHjqhp06bKzc0t61IAlHMEOQAAAEsR5ABc15KSkjRy5EjdddddateunfPj3a+//rq6dOmi9u3ba/z48c5PCl0cDVu2bJk6d+6sNm3a6N1339WePXvUp08feTwexcTEOMt///33FRUVpZiYGLVu3Vo9evTQZ5995rQfP35cjz32mNq2bavu3bvrn//8p9O2Z88eRUZGqlWrVurQoYPz1+qHDBkiSWrTpo1CQ0O1a9euImsGUHER5ABct/Ly8vToo48qODhYGzdu1ObNmxUREaH3339fy5cv1zvvvKP169crIyMjXziTpK+++krr1q3T7Nmz9eKLL2rBggV66623tGrVKn344Yf64osvnL579uzRr371K33++ef6y1/+opEjR+r06dOSpCeffFI33nijtmzZorlz52rWrFlO0Js6daoeeOAB7dy5Ux999JF69uwpSfr73/8u6cJvUe7atUuhoaElqhlAxUOQA3Dd2rNnj06cOKHx48eratWq8vPzk8fjUXx8vP74xz/qpptuUrVq1fTkk09q9erV+e5JGzFihPz8/NSxY0dVrVpVvXv3VmBgoOrXry+Px6N9+/Y5fQMCAvTggw+qcuXKioiI0C233KJNmzY5v9M5duxY+fn5KSQkRAMHDlRcXJwkqVKlSjp8+LBSU1NVrVo1tWzZstBtKUnNACoeghyA61ZSUpKCg4NVqVKlfNNPnDihBg0aOI8bNGig3NxcpaSkONMu/hC7JPn5+V3yOCMjw3lcv35954faJSk4OFgnTpzQiRMnVKtWLVWvXj1f2/HjxyVdGJE7ePCgevbsqQEDBujjjz8udFtKUjOAiocgB+C6FRQUpKSkpEtGrerVq6ejR486jxMTE1WpUqV8Ye1yHD9+XMYY53FSUpLq1aunevXq6cyZM0pPT8/XVr9+fUlSo0aNnEutf/rTn/SXv/xFGRkZ+ULhtaoZwPWBIAfgutWiRQvVrVtXL7/8sjIyMpSVlaUvv/xSvXv31ttvv60ff/xR586d0+zZs9WzZ89LRu5KKjU1Ve+8845ycnL04YcfKiEhQZ07d1ZQUJBCQ0M1a9YsZWVl6bvvvtN7772nvn37SpLi4uKUmpoql8ulmjVrSpJcLpcCAgLkcrn0448/Ouu42jUDuD5wBgBw3fL19dWCBQs0ZcoUdenSRZLUp08fPfPMMzp+/LiGDBmirKwsdezYUc8999wVr6dFixY6dOiQ7rrrLtWpU0dz585V7dq1JUmzZs1SdHS0wsLCVLNmTY0aNUodOnSQJG3ZskUvvfSSMjMzFRwcrNmzZ+v/a8+OTRiIgSgK7mXqT5E6UFHKFQhUocBOnRoOzPpmKtjwsb+UEhERvfdorcU5J8YYUWu99WbgP1yvzz0AgK/svWOtFXPOX58CPJBpFQAgKSEHAJCUaRUAICkfOQCApIQcAEBSQg4AICkhBwCQlJADAEjqDSgsLLnM1e9ZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReWndigifUW6"
      },
      "source": [
        "Investigando o composto **cacb2b860**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "_SUO4RmdYIwW",
        "outputId": "2c4cfe6c-9236-4ebd-a66c-2fd65bf376fe"
      },
      "source": [
        "cod_composto = 'cacb2b860'\n",
        "cod_tratamento = 'com_droga'\n",
        "dados.query('composto == @cod_composto and tratamento == @cod_tratamento')\n",
        "#Buscando na base de dados culturas que tenham recebido o tratamento com droga e o composto cacb2b860\n"
      ],
      "execution_count": 10,
      "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",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, tratamento, tempo, dose, composto, g-0, g-1, g-2, g-3, g-4, g-5, g-6, g-7, g-8, g-9, g-10, g-11, g-12, g-13, g-14, g-15, g-16, g-17, g-18, g-19, g-20, g-21, g-22, g-23, g-24, g-25, g-26, g-27, g-28, g-29, g-30, g-31, g-32, g-33, g-34, g-35, g-36, g-37, g-38, g-39, g-40, g-41, g-42, g-43, g-44, g-45, g-46, g-47, g-48, g-49, g-50, g-51, g-52, g-53, g-54, g-55, g-56, g-57, g-58, g-59, g-60, g-61, g-62, g-63, g-64, g-65, g-66, g-67, g-68, g-69, g-70, g-71, g-72, g-73, g-74, g-75, g-76, g-77, g-78, g-79, g-80, g-81, g-82, g-83, g-84, g-85, g-86, g-87, g-88, g-89, g-90, g-91, g-92, g-93, g-94, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "O8Eyz8Obawgn",
        "outputId": "9174cf04-dfc8-49ba-a77a-cb65cdab80ee"
      },
      "source": [
        "cod_tratamento = 'com_controle'\n",
        "dados.query('composto != @cod_composto and tratamento == @cod_tratamento')\n",
        "#Procurando por culturas que tenham recebido o tratamento com controle e alguma outro composto"
      ],
      "execution_count": 11,
      "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",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, tratamento, tempo, dose, composto, g-0, g-1, g-2, g-3, g-4, g-5, g-6, g-7, g-8, g-9, g-10, g-11, g-12, g-13, g-14, g-15, g-16, g-17, g-18, g-19, g-20, g-21, g-22, g-23, g-24, g-25, g-26, g-27, g-28, g-29, g-30, g-31, g-32, g-33, g-34, g-35, g-36, g-37, g-38, g-39, g-40, g-41, g-42, g-43, g-44, g-45, g-46, g-47, g-48, g-49, g-50, g-51, g-52, g-53, g-54, g-55, g-56, g-57, g-58, g-59, g-60, g-61, g-62, g-63, g-64, g-65, g-66, g-67, g-68, g-69, g-70, g-71, g-72, g-73, g-74, g-75, g-76, g-77, g-78, g-79, g-80, g-81, g-82, g-83, g-84, g-85, g-86, g-87, g-88, g-89, g-90, g-91, g-92, g-93, g-94, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZLe5qTmvYUQ"
      },
      "source": [
        "O composto **cacb2b860** foi usado somente no de controle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "BaadCTxRyjG6",
        "outputId": "8f9a9139-14a1-4aa8-f424-da4fba601ad7"
      },
      "source": [
        "indv_controle = dados.query('composto == @cod_composto and tratamento == @cod_tratamento')\n",
        "indv_controle_descritivo = indv_controle.describe()\n",
        "indv_controle"
      ],
      "execution_count": 12,
      "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>25</th>\n",
              "      <td>id_0054388ec</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.6696</td>\n",
              "      <td>-0.2718</td>\n",
              "      <td>-1.2230</td>\n",
              "      <td>-0.6226</td>\n",
              "      <td>-0.7220</td>\n",
              "      <td>0.1588</td>\n",
              "      <td>0.7785</td>\n",
              "      <td>0.7062</td>\n",
              "      <td>-0.7951</td>\n",
              "      <td>1.3770</td>\n",
              "      <td>0.2856</td>\n",
              "      <td>-0.8331</td>\n",
              "      <td>0.3879</td>\n",
              "      <td>1.3290</td>\n",
              "      <td>-0.4746</td>\n",
              "      <td>0.0857</td>\n",
              "      <td>-0.2745</td>\n",
              "      <td>-0.1093</td>\n",
              "      <td>0.2603</td>\n",
              "      <td>0.7386</td>\n",
              "      <td>-0.6665</td>\n",
              "      <td>-1.6520</td>\n",
              "      <td>-0.9262</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.6310</td>\n",
              "      <td>-0.2933</td>\n",
              "      <td>0.2043</td>\n",
              "      <td>-2.2070</td>\n",
              "      <td>0.1383</td>\n",
              "      <td>-0.4966</td>\n",
              "      <td>0.6922</td>\n",
              "      <td>-0.4077</td>\n",
              "      <td>0.2303</td>\n",
              "      <td>-0.0456</td>\n",
              "      <td>-0.7447</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6976</td>\n",
              "      <td>-0.0178</td>\n",
              "      <td>0.3768</td>\n",
              "      <td>0.4318</td>\n",
              "      <td>-0.1051</td>\n",
              "      <td>0.3933</td>\n",
              "      <td>-0.3189</td>\n",
              "      <td>0.4128</td>\n",
              "      <td>-0.2116</td>\n",
              "      <td>-1.0410</td>\n",
              "      <td>0.7798</td>\n",
              "      <td>0.1579</td>\n",
              "      <td>1.7250</td>\n",
              "      <td>1.4490</td>\n",
              "      <td>0.9117</td>\n",
              "      <td>-0.0754</td>\n",
              "      <td>-0.8396</td>\n",
              "      <td>0.7640</td>\n",
              "      <td>1.2080</td>\n",
              "      <td>1.1890</td>\n",
              "      <td>0.2114</td>\n",
              "      <td>0.6335</td>\n",
              "      <td>0.3265</td>\n",
              "      <td>-0.1056</td>\n",
              "      <td>0.5478</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.2340</td>\n",
              "      <td>0.5035</td>\n",
              "      <td>-0.6591</td>\n",
              "      <td>0.3023</td>\n",
              "      <td>0.6171</td>\n",
              "      <td>0.6405</td>\n",
              "      <td>0.5429</td>\n",
              "      <td>0.3562</td>\n",
              "      <td>1.3290</td>\n",
              "      <td>0.5573</td>\n",
              "      <td>0.8837</td>\n",
              "      <td>0.5534</td>\n",
              "      <td>0.8976</td>\n",
              "      <td>1.0050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>id_0079af0fb</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.1636</td>\n",
              "      <td>-1.8230</td>\n",
              "      <td>-0.5211</td>\n",
              "      <td>0.3054</td>\n",
              "      <td>-1.1280</td>\n",
              "      <td>0.6041</td>\n",
              "      <td>0.3856</td>\n",
              "      <td>-0.1670</td>\n",
              "      <td>-0.5400</td>\n",
              "      <td>0.2721</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>0.0761</td>\n",
              "      <td>-0.4054</td>\n",
              "      <td>2.9760</td>\n",
              "      <td>-0.7679</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>0.0752</td>\n",
              "      <td>0.5943</td>\n",
              "      <td>0.4266</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0410</td>\n",
              "      <td>-0.1131</td>\n",
              "      <td>0.6593</td>\n",
              "      <td>-0.6890</td>\n",
              "      <td>0.5115</td>\n",
              "      <td>-0.2512</td>\n",
              "      <td>-0.8775</td>\n",
              "      <td>-1.3810</td>\n",
              "      <td>0.1419</td>\n",
              "      <td>-0.0363</td>\n",
              "      <td>0.2559</td>\n",
              "      <td>2.5290</td>\n",
              "      <td>0.2853</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.3626</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.3420</td>\n",
              "      <td>0.2815</td>\n",
              "      <td>-0.7493</td>\n",
              "      <td>-0.2701</td>\n",
              "      <td>0.1205</td>\n",
              "      <td>0.2396</td>\n",
              "      <td>0.3506</td>\n",
              "      <td>-0.9233</td>\n",
              "      <td>0.2490</td>\n",
              "      <td>-0.0824</td>\n",
              "      <td>0.4841</td>\n",
              "      <td>1.1940</td>\n",
              "      <td>-1.1650</td>\n",
              "      <td>-0.4853</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>0.7438</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>-0.2761</td>\n",
              "      <td>-0.1748</td>\n",
              "      <td>0.3385</td>\n",
              "      <td>-0.3500</td>\n",
              "      <td>-0.0309</td>\n",
              "      <td>-0.0409</td>\n",
              "      <td>-0.0735</td>\n",
              "      <td>0.2732</td>\n",
              "      <td>-1.2100</td>\n",
              "      <td>0.0089</td>\n",
              "      <td>0.0317</td>\n",
              "      <td>0.4721</td>\n",
              "      <td>-0.3880</td>\n",
              "      <td>-0.6828</td>\n",
              "      <td>-0.6594</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.3571</td>\n",
              "      <td>-0.1319</td>\n",
              "      <td>0.2187</td>\n",
              "      <td>0.0737</td>\n",
              "      <td>0.6498</td>\n",
              "      <td>-1.4820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>id_007bfbb91</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-1.3200</td>\n",
              "      <td>-1.7340</td>\n",
              "      <td>-0.0741</td>\n",
              "      <td>1.5440</td>\n",
              "      <td>-1.8290</td>\n",
              "      <td>-0.0951</td>\n",
              "      <td>0.3829</td>\n",
              "      <td>-1.8700</td>\n",
              "      <td>-0.0324</td>\n",
              "      <td>2.7670</td>\n",
              "      <td>-0.0438</td>\n",
              "      <td>-0.0147</td>\n",
              "      <td>0.1276</td>\n",
              "      <td>0.2968</td>\n",
              "      <td>-0.0704</td>\n",
              "      <td>-0.3767</td>\n",
              "      <td>2.5790</td>\n",
              "      <td>0.4069</td>\n",
              "      <td>-0.3868</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>1.8180</td>\n",
              "      <td>-2.9390</td>\n",
              "      <td>-0.1150</td>\n",
              "      <td>-0.8009</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>0.5412</td>\n",
              "      <td>-0.0804</td>\n",
              "      <td>-0.4444</td>\n",
              "      <td>2.9230</td>\n",
              "      <td>2.8270</td>\n",
              "      <td>-0.3437</td>\n",
              "      <td>1.6570</td>\n",
              "      <td>-1.9200</td>\n",
              "      <td>0.3442</td>\n",
              "      <td>1.7160</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4009</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2789</td>\n",
              "      <td>0.4465</td>\n",
              "      <td>0.9492</td>\n",
              "      <td>0.6385</td>\n",
              "      <td>1.5510</td>\n",
              "      <td>-0.1220</td>\n",
              "      <td>0.6688</td>\n",
              "      <td>0.4396</td>\n",
              "      <td>0.6947</td>\n",
              "      <td>0.9761</td>\n",
              "      <td>1.2430</td>\n",
              "      <td>1.4770</td>\n",
              "      <td>-0.0081</td>\n",
              "      <td>1.0040</td>\n",
              "      <td>1.4420</td>\n",
              "      <td>-0.3482</td>\n",
              "      <td>0.3297</td>\n",
              "      <td>1.2070</td>\n",
              "      <td>0.0524</td>\n",
              "      <td>0.5346</td>\n",
              "      <td>0.7643</td>\n",
              "      <td>0.4067</td>\n",
              "      <td>0.3817</td>\n",
              "      <td>0.4566</td>\n",
              "      <td>0.7736</td>\n",
              "      <td>0.5316</td>\n",
              "      <td>0.2790</td>\n",
              "      <td>0.9735</td>\n",
              "      <td>0.7412</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>0.7393</td>\n",
              "      <td>1.1020</td>\n",
              "      <td>0.3786</td>\n",
              "      <td>0.2636</td>\n",
              "      <td>-0.5751</td>\n",
              "      <td>0.3362</td>\n",
              "      <td>0.8543</td>\n",
              "      <td>0.9180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>id_008a986b7</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.4860</td>\n",
              "      <td>0.1430</td>\n",
              "      <td>0.5281</td>\n",
              "      <td>-0.0022</td>\n",
              "      <td>-0.2334</td>\n",
              "      <td>-0.6607</td>\n",
              "      <td>0.6459</td>\n",
              "      <td>-0.0109</td>\n",
              "      <td>1.0120</td>\n",
              "      <td>-0.0864</td>\n",
              "      <td>-0.3817</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>0.9435</td>\n",
              "      <td>0.6296</td>\n",
              "      <td>0.3864</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>0.9834</td>\n",
              "      <td>0.4189</td>\n",
              "      <td>0.7980</td>\n",
              "      <td>-0.2132</td>\n",
              "      <td>0.7651</td>\n",
              "      <td>-1.2410</td>\n",
              "      <td>0.0747</td>\n",
              "      <td>-1.5750</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.0840</td>\n",
              "      <td>0.6705</td>\n",
              "      <td>-0.7517</td>\n",
              "      <td>-0.2756</td>\n",
              "      <td>-1.0920</td>\n",
              "      <td>-0.8683</td>\n",
              "      <td>0.3021</td>\n",
              "      <td>-0.3164</td>\n",
              "      <td>-0.2074</td>\n",
              "      <td>0.4587</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2189</td>\n",
              "      <td>-0.5262</td>\n",
              "      <td>-0.2982</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>-1.0040</td>\n",
              "      <td>-0.6726</td>\n",
              "      <td>-0.0321</td>\n",
              "      <td>-0.5398</td>\n",
              "      <td>-0.7518</td>\n",
              "      <td>0.0370</td>\n",
              "      <td>0.7213</td>\n",
              "      <td>-0.3466</td>\n",
              "      <td>0.2354</td>\n",
              "      <td>-0.4432</td>\n",
              "      <td>-0.1020</td>\n",
              "      <td>-0.9391</td>\n",
              "      <td>-1.1970</td>\n",
              "      <td>0.1365</td>\n",
              "      <td>-0.9264</td>\n",
              "      <td>0.5828</td>\n",
              "      <td>-0.7379</td>\n",
              "      <td>-0.4354</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>-0.3771</td>\n",
              "      <td>-0.6525</td>\n",
              "      <td>-0.4169</td>\n",
              "      <td>-1.0640</td>\n",
              "      <td>-0.3331</td>\n",
              "      <td>-0.5761</td>\n",
              "      <td>-0.3085</td>\n",
              "      <td>-0.1866</td>\n",
              "      <td>0.7629</td>\n",
              "      <td>0.3288</td>\n",
              "      <td>-0.9915</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.4979</td>\n",
              "      <td>0.5281</td>\n",
              "      <td>-0.9245</td>\n",
              "      <td>-0.9367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>id_009d8340f</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.4471</td>\n",
              "      <td>-0.0839</td>\n",
              "      <td>-0.3864</td>\n",
              "      <td>0.3196</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>-0.3156</td>\n",
              "      <td>-0.1187</td>\n",
              "      <td>1.1610</td>\n",
              "      <td>0.5838</td>\n",
              "      <td>-0.2996</td>\n",
              "      <td>-0.3995</td>\n",
              "      <td>1.0700</td>\n",
              "      <td>0.2969</td>\n",
              "      <td>-0.2785</td>\n",
              "      <td>0.9678</td>\n",
              "      <td>-0.1409</td>\n",
              "      <td>1.0850</td>\n",
              "      <td>-0.0656</td>\n",
              "      <td>-0.6502</td>\n",
              "      <td>0.3527</td>\n",
              "      <td>0.1998</td>\n",
              "      <td>0.0975</td>\n",
              "      <td>0.0563</td>\n",
              "      <td>-0.3578</td>\n",
              "      <td>-0.0657</td>\n",
              "      <td>0.5316</td>\n",
              "      <td>0.6573</td>\n",
              "      <td>-0.5355</td>\n",
              "      <td>1.2380</td>\n",
              "      <td>-0.4994</td>\n",
              "      <td>-0.8140</td>\n",
              "      <td>0.2567</td>\n",
              "      <td>-0.8155</td>\n",
              "      <td>1.1180</td>\n",
              "      <td>-0.2501</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.5214</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>-0.5903</td>\n",
              "      <td>0.1332</td>\n",
              "      <td>-0.3517</td>\n",
              "      <td>-0.3276</td>\n",
              "      <td>-0.4955</td>\n",
              "      <td>0.1359</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>-0.1126</td>\n",
              "      <td>-0.0874</td>\n",
              "      <td>1.2220</td>\n",
              "      <td>-0.0641</td>\n",
              "      <td>0.0246</td>\n",
              "      <td>-0.5659</td>\n",
              "      <td>-0.7402</td>\n",
              "      <td>1.1170</td>\n",
              "      <td>-0.0612</td>\n",
              "      <td>-1.3040</td>\n",
              "      <td>-0.4025</td>\n",
              "      <td>0.3760</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>0.8064</td>\n",
              "      <td>-0.0170</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>0.2671</td>\n",
              "      <td>0.3311</td>\n",
              "      <td>-0.4572</td>\n",
              "      <td>0.4795</td>\n",
              "      <td>0.3939</td>\n",
              "      <td>0.7665</td>\n",
              "      <td>0.7932</td>\n",
              "      <td>-0.6804</td>\n",
              "      <td>-0.0435</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>0.2000</td>\n",
              "      <td>-0.4326</td>\n",
              "      <td>0.9364</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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23729</th>\n",
              "      <td>id_ff1f9e5fc</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.0465</td>\n",
              "      <td>0.6430</td>\n",
              "      <td>-0.2453</td>\n",
              "      <td>0.3521</td>\n",
              "      <td>0.6195</td>\n",
              "      <td>-0.6659</td>\n",
              "      <td>0.8263</td>\n",
              "      <td>0.5933</td>\n",
              "      <td>0.1388</td>\n",
              "      <td>-0.2246</td>\n",
              "      <td>-0.6127</td>\n",
              "      <td>-0.1687</td>\n",
              "      <td>1.5170</td>\n",
              "      <td>0.6243</td>\n",
              "      <td>-0.8399</td>\n",
              "      <td>-0.1881</td>\n",
              "      <td>-0.1583</td>\n",
              "      <td>0.0622</td>\n",
              "      <td>-0.1170</td>\n",
              "      <td>0.7722</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.7347</td>\n",
              "      <td>-0.4820</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0938</td>\n",
              "      <td>-0.6062</td>\n",
              "      <td>0.6108</td>\n",
              "      <td>-0.4900</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>0.3029</td>\n",
              "      <td>-0.1369</td>\n",
              "      <td>0.1946</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>1.1030</td>\n",
              "      <td>0.3444</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4342</td>\n",
              "      <td>0.0541</td>\n",
              "      <td>0.9346</td>\n",
              "      <td>0.7595</td>\n",
              "      <td>-0.0139</td>\n",
              "      <td>0.2990</td>\n",
              "      <td>-0.7176</td>\n",
              "      <td>0.8572</td>\n",
              "      <td>0.2990</td>\n",
              "      <td>-0.2060</td>\n",
              "      <td>0.9029</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.0802</td>\n",
              "      <td>0.0686</td>\n",
              "      <td>-0.3934</td>\n",
              "      <td>0.2344</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.0144</td>\n",
              "      <td>-1.0080</td>\n",
              "      <td>0.8748</td>\n",
              "      <td>1.2900</td>\n",
              "      <td>0.5188</td>\n",
              "      <td>0.5824</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>0.7578</td>\n",
              "      <td>1.1910</td>\n",
              "      <td>-0.3264</td>\n",
              "      <td>0.6882</td>\n",
              "      <td>0.7486</td>\n",
              "      <td>0.6438</td>\n",
              "      <td>0.6085</td>\n",
              "      <td>0.1621</td>\n",
              "      <td>-0.2065</td>\n",
              "      <td>0.8314</td>\n",
              "      <td>-0.1891</td>\n",
              "      <td>-0.0074</td>\n",
              "      <td>0.2131</td>\n",
              "      <td>-0.1419</td>\n",
              "      <td>-0.2422</td>\n",
              "      <td>0.0457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23747</th>\n",
              "      <td>id_ff469c482</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.6204</td>\n",
              "      <td>-1.3800</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>-0.6573</td>\n",
              "      <td>-0.8408</td>\n",
              "      <td>-0.2447</td>\n",
              "      <td>1.3490</td>\n",
              "      <td>-0.5155</td>\n",
              "      <td>1.2610</td>\n",
              "      <td>-0.1790</td>\n",
              "      <td>-0.7182</td>\n",
              "      <td>-0.7885</td>\n",
              "      <td>0.3022</td>\n",
              "      <td>2.0140</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>-0.7325</td>\n",
              "      <td>-0.1553</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>-0.1069</td>\n",
              "      <td>-0.7247</td>\n",
              "      <td>1.3430</td>\n",
              "      <td>1.5010</td>\n",
              "      <td>-1.3370</td>\n",
              "      <td>0.7190</td>\n",
              "      <td>-0.0843</td>\n",
              "      <td>0.4841</td>\n",
              "      <td>-0.9104</td>\n",
              "      <td>1.3990</td>\n",
              "      <td>0.7447</td>\n",
              "      <td>-1.2110</td>\n",
              "      <td>0.6562</td>\n",
              "      <td>0.1265</td>\n",
              "      <td>0.3036</td>\n",
              "      <td>2.2600</td>\n",
              "      <td>-1.3770</td>\n",
              "      <td>...</td>\n",
              "      <td>0.9633</td>\n",
              "      <td>-0.4057</td>\n",
              "      <td>-0.2206</td>\n",
              "      <td>0.5111</td>\n",
              "      <td>-0.8666</td>\n",
              "      <td>0.4692</td>\n",
              "      <td>0.8903</td>\n",
              "      <td>0.6648</td>\n",
              "      <td>0.2922</td>\n",
              "      <td>0.2440</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>1.2420</td>\n",
              "      <td>1.0430</td>\n",
              "      <td>1.4310</td>\n",
              "      <td>1.0040</td>\n",
              "      <td>1.0320</td>\n",
              "      <td>0.7345</td>\n",
              "      <td>0.8025</td>\n",
              "      <td>0.7174</td>\n",
              "      <td>0.1932</td>\n",
              "      <td>-0.2609</td>\n",
              "      <td>0.3501</td>\n",
              "      <td>0.5916</td>\n",
              "      <td>0.9193</td>\n",
              "      <td>0.1398</td>\n",
              "      <td>0.0243</td>\n",
              "      <td>0.4820</td>\n",
              "      <td>-0.4428</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.4166</td>\n",
              "      <td>0.8209</td>\n",
              "      <td>0.7366</td>\n",
              "      <td>0.5468</td>\n",
              "      <td>1.1450</td>\n",
              "      <td>0.5254</td>\n",
              "      <td>0.6224</td>\n",
              "      <td>-0.9630</td>\n",
              "      <td>0.8872</td>\n",
              "      <td>-0.0742</td>\n",
              "      <td>-0.6777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23768</th>\n",
              "      <td>id_ff89331ca</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.1051</td>\n",
              "      <td>0.4335</td>\n",
              "      <td>0.3648</td>\n",
              "      <td>-0.0525</td>\n",
              "      <td>-0.3632</td>\n",
              "      <td>-0.3228</td>\n",
              "      <td>-2.3190</td>\n",
              "      <td>-0.5294</td>\n",
              "      <td>0.1878</td>\n",
              "      <td>0.5047</td>\n",
              "      <td>1.3820</td>\n",
              "      <td>0.6107</td>\n",
              "      <td>2.4790</td>\n",
              "      <td>0.4347</td>\n",
              "      <td>0.9422</td>\n",
              "      <td>-0.1042</td>\n",
              "      <td>-0.2611</td>\n",
              "      <td>1.5760</td>\n",
              "      <td>-0.0232</td>\n",
              "      <td>-0.2455</td>\n",
              "      <td>0.8058</td>\n",
              "      <td>-2.4180</td>\n",
              "      <td>-0.5838</td>\n",
              "      <td>-0.1626</td>\n",
              "      <td>-0.8609</td>\n",
              "      <td>-0.2021</td>\n",
              "      <td>0.1494</td>\n",
              "      <td>0.6618</td>\n",
              "      <td>1.3080</td>\n",
              "      <td>-0.7822</td>\n",
              "      <td>0.3923</td>\n",
              "      <td>-0.4608</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>0.3173</td>\n",
              "      <td>0.7622</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2340</td>\n",
              "      <td>-0.1665</td>\n",
              "      <td>-0.0318</td>\n",
              "      <td>0.8694</td>\n",
              "      <td>0.4048</td>\n",
              "      <td>0.1281</td>\n",
              "      <td>0.0164</td>\n",
              "      <td>1.0670</td>\n",
              "      <td>1.5790</td>\n",
              "      <td>0.2428</td>\n",
              "      <td>1.1330</td>\n",
              "      <td>-1.2310</td>\n",
              "      <td>0.3626</td>\n",
              "      <td>-0.4737</td>\n",
              "      <td>0.2937</td>\n",
              "      <td>0.1638</td>\n",
              "      <td>-0.1554</td>\n",
              "      <td>0.9236</td>\n",
              "      <td>0.0937</td>\n",
              "      <td>0.9598</td>\n",
              "      <td>0.4738</td>\n",
              "      <td>-0.2608</td>\n",
              "      <td>0.3577</td>\n",
              "      <td>-1.2040</td>\n",
              "      <td>0.8125</td>\n",
              "      <td>1.4260</td>\n",
              "      <td>0.2223</td>\n",
              "      <td>0.3079</td>\n",
              "      <td>1.1490</td>\n",
              "      <td>1.7320</td>\n",
              "      <td>-0.1219</td>\n",
              "      <td>0.4885</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.9434</td>\n",
              "      <td>-0.4779</td>\n",
              "      <td>0.6951</td>\n",
              "      <td>0.9517</td>\n",
              "      <td>-0.8110</td>\n",
              "      <td>-0.1748</td>\n",
              "      <td>-0.3626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23793</th>\n",
              "      <td>id_ffd26f361</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.6008</td>\n",
              "      <td>0.2781</td>\n",
              "      <td>-0.3319</td>\n",
              "      <td>-0.8782</td>\n",
              "      <td>0.9281</td>\n",
              "      <td>0.7535</td>\n",
              "      <td>0.5971</td>\n",
              "      <td>1.6350</td>\n",
              "      <td>-0.3146</td>\n",
              "      <td>0.4984</td>\n",
              "      <td>-1.1890</td>\n",
              "      <td>0.3772</td>\n",
              "      <td>-0.8196</td>\n",
              "      <td>0.1083</td>\n",
              "      <td>-2.0350</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.7586</td>\n",
              "      <td>-0.4024</td>\n",
              "      <td>-0.0449</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.2358</td>\n",
              "      <td>0.7919</td>\n",
              "      <td>-0.8775</td>\n",
              "      <td>0.4232</td>\n",
              "      <td>1.8460</td>\n",
              "      <td>-2.0940</td>\n",
              "      <td>0.1438</td>\n",
              "      <td>-0.6604</td>\n",
              "      <td>0.7243</td>\n",
              "      <td>0.0338</td>\n",
              "      <td>-0.0182</td>\n",
              "      <td>-0.2022</td>\n",
              "      <td>0.8827</td>\n",
              "      <td>0.3147</td>\n",
              "      <td>-0.9263</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1934</td>\n",
              "      <td>1.2240</td>\n",
              "      <td>-0.1178</td>\n",
              "      <td>0.1459</td>\n",
              "      <td>-0.1242</td>\n",
              "      <td>0.3106</td>\n",
              "      <td>-0.7959</td>\n",
              "      <td>0.5681</td>\n",
              "      <td>0.3663</td>\n",
              "      <td>0.4874</td>\n",
              "      <td>-0.0548</td>\n",
              "      <td>0.1810</td>\n",
              "      <td>1.3470</td>\n",
              "      <td>1.6210</td>\n",
              "      <td>-0.8260</td>\n",
              "      <td>0.3377</td>\n",
              "      <td>0.3264</td>\n",
              "      <td>0.9241</td>\n",
              "      <td>0.3012</td>\n",
              "      <td>1.2980</td>\n",
              "      <td>-0.2789</td>\n",
              "      <td>0.7019</td>\n",
              "      <td>-0.3528</td>\n",
              "      <td>0.7383</td>\n",
              "      <td>1.0300</td>\n",
              "      <td>0.6527</td>\n",
              "      <td>1.6750</td>\n",
              "      <td>-0.5046</td>\n",
              "      <td>0.2942</td>\n",
              "      <td>0.2950</td>\n",
              "      <td>1.2470</td>\n",
              "      <td>0.9569</td>\n",
              "      <td>-0.2065</td>\n",
              "      <td>-0.4918</td>\n",
              "      <td>0.7863</td>\n",
              "      <td>0.0504</td>\n",
              "      <td>0.8813</td>\n",
              "      <td>0.7757</td>\n",
              "      <td>-0.5272</td>\n",
              "      <td>0.7082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1866 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "25     id_0054388ec  com_controle     48   D1  ...  0.8837  0.5534  0.8976  1.0050\n",
              "38     id_0079af0fb  com_controle     24   D1  ...  0.2187  0.0737  0.6498 -1.4820\n",
              "40     id_007bfbb91  com_controle     24   D2  ... -0.5751  0.3362  0.8543  0.9180\n",
              "47     id_008a986b7  com_controle     48   D2  ... -0.4979  0.5281 -0.9245 -0.9367\n",
              "63     id_009d8340f  com_controle     24   D2  ...  0.0949  0.2000 -0.4326  0.9364\n",
              "...             ...           ...    ...  ...  ...     ...     ...     ...     ...\n",
              "23729  id_ff1f9e5fc  com_controle     24   D1  ...  0.2131 -0.1419 -0.2422  0.0457\n",
              "23747  id_ff469c482  com_controle     72   D1  ... -0.9630  0.8872 -0.0742 -0.6777\n",
              "23768  id_ff89331ca  com_controle     72   D2  ...  0.9517 -0.8110 -0.1748 -0.3626\n",
              "23793  id_ffd26f361  com_controle     48   D2  ...  0.8813  0.7757 -0.5272  0.7082\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "\n",
              "[1866 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "U92HbzXrMhcY",
        "outputId": "c4899a00-b0d6-42bd-f000-a53bae6895f7"
      },
      "source": [
        "indv_controle_descritivo_cells  = indv_controle.loc[:,'c-0':'c-99'].describe()\n",
        "\n",
        "cod_stdmax = indv_controle_descritivo_cells.T['std'].max()\n",
        "cod_stdmin = indv_controle_descritivo_cells.T['std'].min()\n",
        "\n",
        "indv_controle_descritivo_cells_std = indv_controle_descritivo.T.query('std == @cod_stdmax or std == @cod_stdmin')\n",
        "indv_controle_descritivo_cells_std"
      ],
      "execution_count": 13,
      "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>c-18</th>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.224250</td>\n",
              "      <td>0.981514</td>\n",
              "      <td>-9.369</td>\n",
              "      <td>-0.13080</td>\n",
              "      <td>0.32130</td>\n",
              "      <td>0.730775</td>\n",
              "      <td>3.412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-98</th>\n",
              "      <td>1866.0</td>\n",
              "      <td>0.286888</td>\n",
              "      <td>0.620762</td>\n",
              "      <td>-3.321</td>\n",
              "      <td>-0.09145</td>\n",
              "      <td>0.34435</td>\n",
              "      <td>0.711075</td>\n",
              "      <td>1.912</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       count      mean       std    min      25%      50%       75%    max\n",
              "c-18  1866.0  0.224250  0.981514 -9.369 -0.13080  0.32130  0.730775  3.412\n",
              "c-98  1866.0  0.286888  0.620762 -3.321 -0.09145  0.34435  0.711075  1.912"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "X2AcpMGI6ezV",
        "outputId": "2675f804-0d12-4bbe-c17e-015020e43bfd"
      },
      "source": [
        "indv_controle_descritivo_gen = indv_controle.loc[:,'g-0':'g-771'].describe()\n",
        "\n",
        "cod_stdmax = indv_controle_descritivo_gen.T['std'].max()\n",
        "cod_stdmin = indv_controle_descritivo_gen.T['std'].min()\n",
        "\n",
        "std_indv_controle_gen = indv_controle_descritivo.T.query('std == @cod_stdmax or std == @cod_stdmin')\n",
        "\n",
        "std_indv_controle_gen"
      ],
      "execution_count": 14,
      "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-307</th>\n",
              "      <td>1866.0</td>\n",
              "      <td>-0.037620</td>\n",
              "      <td>0.438798</td>\n",
              "      <td>-2.827</td>\n",
              "      <td>-0.185500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0597</td>\n",
              "      <td>2.392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-370</th>\n",
              "      <td>1866.0</td>\n",
              "      <td>-0.501596</td>\n",
              "      <td>1.493926</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.582625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1913</td>\n",
              "      <td>2.315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count      mean       std     min       25%  50%     75%    max\n",
              "g-307  1866.0 -0.037620  0.438798  -2.827 -0.185500  0.0  0.0597  2.392\n",
              "g-370  1866.0 -0.501596  1.493926 -10.000 -0.582625  0.0  0.1913  2.315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "YxLiAAFg_4Is",
        "outputId": "1a047860-dbbd-4474-8ace-f91e94858a96"
      },
      "source": [
        "sns.set()\n",
        "sns.set_theme(font_scale=1, color_codes=True, rc=None)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "# ax = sns.histplot(std_indv_controle['std'],x='g-307')\n",
        "# ax = sns.histplot(data=std_indv_controle, kde=True, stat='count')\n",
        "ax = sns.kdeplot(data=indv_controle[['g-307','g-370','c-18','c-98']])\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "MkJgwnxkNuXa",
        "outputId": "3fb305f7-5287-4600-86bb-191bbb3d3167"
      },
      "source": [
        "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(15,7))\n",
        "\n",
        "ax1.set_title('Genes controle que \\n apresentaram maior e menor desvio padrão')\n",
        "ax1.set_ylabel('Frequência')\n",
        "ax1.set_xlabel(' ')\n",
        "sns.kdeplot(data=indv_controle[['g-307','g-370']], ax=ax1)\n",
        "\n",
        "ax2.set_title('g-307')\n",
        "ax2.set_ylabel('Frequência')\n",
        "ax2.set_xlabel('Menor Desvio')\n",
        "sns.histplot(data=indv_controle['g-307'], ax=ax2, bins=25)\n",
        "\n",
        "ax3.set_title('g-370')\n",
        "ax3.set_ylabel('Frequência')\n",
        "ax3.set_xlabel('Maior Desvio')\n",
        "sns.histplot(data=indv_controle['g-370'], ax=ax3, bins=25)\n",
        "\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf6fd4cc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "QMY5vaHb9FXr",
        "outputId": "f9e37514-7c66-4d7b-cd2a-bf5a37f5f14d"
      },
      "source": [
        "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(15,7))\n",
        "\n",
        "ax1.set_title('Celulas controle que \\n apresentaram maior e menor desvio padrão')\n",
        "ax1.set_ylabel('Frequência')\n",
        "ax1.set_xlabel(' ')\n",
        "sns.kdeplot(data=indv_controle[['c-18','c-98']], ax=ax1)\n",
        "\n",
        "ax2.set_title('c-18')\n",
        "ax2.set_ylabel('Frequência')\n",
        "ax2.set_xlabel('Maior Desvio')\n",
        "sns.histplot(data=indv_controle['c-18'], ax=ax2, bins=20)\n",
        "\n",
        "ax3.set_title('c-98')\n",
        "ax3.set_ylabel('Frequência')\n",
        "ax3.set_xlabel('Menor Desvio')\n",
        "sns.histplot(data=indv_controle['c-98'], ax=ax3, bins=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf6fb21bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}