ImersãoAlura_Aula2_Desafios.ipynb

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      "name": "ImersãoAlura_Aula2_Desafios.ipynb",
      "provenance": [],
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzlVTHo7nUlN"
      },
      "source": [
        "#Imersão Dados 3°Edição.\n",
        "##### Alura (03 a 07 Maio 2021)\n",
        "##### *por Luciana Lanzoni Menges*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzRIrDrHpfZW"
      },
      "source": [
        "Imersão de Dados é um evento organizado pela Alura (www.alura.com.br), de uma semana, onde será mostrado de forma prática um projeto de ciência de dados do zero. Nesta edição serão explorados os dados de ***Drug Discovery***, que é o processo de descoberta de novos medicamentos por pesquisadores. \n",
        "Esse projeto foi inspirado em um desafio do Laboratory innovation science at Harvard disponibilizando os dados em uma competição no kaggle.\n",
        "Em cada uma das aulas diárias, além do conteúdo, são propostos desafios para serem resolvidos pelos participantes. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lyggfFjHfyjr"
      },
      "source": [
        "# Aula 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYqQbLZjSMh0"
      },
      "source": [
        "####Importando Base dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "otDcCJ1t1nIP",
        "outputId": "240aa6a9-dbb9-4f62-ae09-76f7c55fe1bf"
      },
      "source": [
        "import pandas as pd\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "dados = pd.read_csv(url_dados, compression='zip')\n",
        "dados.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>24</td>\n",
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              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
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              "      <td>0.6280</td>\n",
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              "      <td>1.5540</td>\n",
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              "      <td>1.2390</td>\n",
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              "      <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",
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              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
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              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
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              "      <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",
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              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
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              "      <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",
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              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
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              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
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              "      <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",
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              "      <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",
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              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
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              "      <td>-1.7750</td>\n",
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              "      <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": 253
        },
        "id": "Vur3yWZNGK16",
        "outputId": "c1bbd07f-f661-40e3-c026-c7138e8c013d"
      },
      "source": [
        "Nomes_colunas = dados.columns.str.replace('-','_')\n",
        "dados.columns = Nomes_colunas\n",
        "dados.head()\n",
        "#Retirado o hífen do nome das colunas.\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>droga</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>...</th>\n",
              "      <th>c_60</th>\n",
              "      <th>c_61</th>\n",
              "      <th>c_62</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c_96    c_97    c_98    c_99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SX4KI1BSCYJ"
      },
      "source": [
        "####Alterando nome da coluna 'droga'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxKVk22APvDv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "d87b442a-2dda-4ebb-e399-66eae2d97747"
      },
      "source": [
        "mapa = {'droga': 'composto'}\n",
        "dados.rename(columns=mapa,inplace=True)\n",
        "dados.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>...</th>\n",
              "      <th>c_60</th>\n",
              "      <th>c_61</th>\n",
              "      <th>c_62</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c_96    c_97    c_98    c_99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G06K4AeVfxnc"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSQtLuuCfQAr"
      },
      "source": [
        "###Desafios\n",
        "####Desafio 01: Ordenar o gráfico countplot. (E todos os outros gráficos com o countplot)\n",
        "####Desafio 02: Melhorar a visualização, alterando tamanho da fonte, ordenando os valores.\n",
        "####Desafio 03: Plotar os histogramas com seaborn\n",
        "####Desafio 04: Estudar sobre as estatísticas retornadas no .describe\n",
        "####Desafio 05: Refletir sobre a manipulação dos tamanho das visualizações. Distorções oque são?\n",
        "####Desafio 06: Fazer outras análises com o boxplot e até com o histograma.\n",
        "####Desafio 07: Resumo do que vc aprendeu com os dados. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-Nc02MsBaF_"
      },
      "source": [
        "#####Utilizando a biblioteca Seaborn e Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3tAa0lEfMlr"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boqGi7KnCArn"
      },
      "source": [
        "#####Mostrar um gráfico com os 5 Compostos mais utilizados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGxwOaS0C_AN",
        "outputId": "ddccf2e8-5240-4bb5-a6ae-8d44dd15b832"
      },
      "source": [
        "dados['composto'].value_counts()\n",
        "#Todos os compostos com a contagem de vezes que apareceram"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    1866\n",
              "87d714366     718\n",
              "9f80f3f77     246\n",
              "8b87a7a83     203\n",
              "5628cb3ee     202\n",
              "             ... \n",
              "f2cc15da6       1\n",
              "153840764       1\n",
              "c7e535136       1\n",
              "b01cf573a       1\n",
              "35519ef6d       1\n",
              "Name: composto, Length: 3289, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr0gXPKKBTWU",
        "outputId": "7bb59447-cd15-4de1-8ae0-8c6f57bff9ed"
      },
      "source": [
        "cod_compostos = dados['composto'].value_counts().index[0:5]\n",
        "cod_compostos\n",
        "# Será utilizado apenas os nomes dos 5 compostos mais utilizados"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['cacb2b860', '87d714366', '9f80f3f77', '8b87a7a83', '5628cb3ee'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-szYyqtyD0xh"
      },
      "source": [
        "Gráfico criado em aula."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "r4IgFJtQCpeG",
        "outputId": "5d65c22e-df84-4c8c-e8af-afe1e210244f"
      },
      "source": [
        "sns.countplot(x = 'composto', data=dados.query('composto in @cod_compostos'))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3fc7bf9e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtKXroo3jnOj"
      },
      "source": [
        "####Desafio 01\n",
        "Ordenar o gráfico countplot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5gmVz60I1hh"
      },
      "source": [
        "####Desafio 02\n",
        "Melhorar a visualização, alterando tamanho da fonte, ordenando os valores."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Prx3PIyHD8uB"
      },
      "source": [
        "#####Novo gráfico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "1xO9bgvDD746",
        "outputId": "ae463dff-83d1-48b8-dbe0-bf68beba05d5"
      },
      "source": [
        "sns.set()\n",
        "plt.figure(figsize=(10,6))\n",
        "ax = sns.countplot(\n",
        "    x = 'composto', \n",
        "    data=dados.query('composto in @cod_compostos'), \n",
        "    order= dados['composto'].value_counts().head().index, \n",
        "    palette= sns.color_palette(\"flare\")\n",
        "    )\n",
        "ax.set_title('Os 5 Compostos mais utilizados nos experimentos', size=18, weight='bold')\n",
        "ax.set_xlabel('Nome dos compostos', size=13, style='italic')\n",
        "ax.set_ylabel('Total de amostras',size=13, style='italic')\n",
        "sns.despine()  # remove as linhas de cima e da direita\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMhZDWTHWnFE"
      },
      "source": [
        "Os valores foram ordenados de forma decrescente e alterado a paleta de cores. É possível alterar o layout das informações de cada eixo. Neste gráfico alterei o tamanho da fonte, utilizando italico para os eixos e negrito no Título. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nDzLKc9Xgm5"
      },
      "source": [
        "####Desafio 03 \n",
        "Plotar os histogramas com seaborn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S0EpHafyLHg"
      },
      "source": [
        "Abaixo são os valores estatísticos dos experimentos dos genes. São ao todo 772 tipos de genes diferentes testasdos. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "QxWKnZbVXmSH",
        "outputId": "4c91afcc-9047-4c69-fcf2-b099316681d4"
      },
      "source": [
        "dados.loc[:,'g_0':'g_771'].describe()"
      ],
      "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>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>g_35</th>\n",
              "      <th>g_36</th>\n",
              "      <th>g_37</th>\n",
              "      <th>g_38</th>\n",
              "      <th>g_39</th>\n",
              "      <th>...</th>\n",
              "      <th>g_732</th>\n",
              "      <th>g_733</th>\n",
              "      <th>g_734</th>\n",
              "      <th>g_735</th>\n",
              "      <th>g_736</th>\n",
              "      <th>g_737</th>\n",
              "      <th>g_738</th>\n",
              "      <th>g_739</th>\n",
              "      <th>g_740</th>\n",
              "      <th>g_741</th>\n",
              "      <th>g_742</th>\n",
              "      <th>g_743</th>\n",
              "      <th>g_744</th>\n",
              "      <th>g_745</th>\n",
              "      <th>g_746</th>\n",
              "      <th>g_747</th>\n",
              "      <th>g_748</th>\n",
              "      <th>g_749</th>\n",
              "      <th>g_750</th>\n",
              "      <th>g_751</th>\n",
              "      <th>g_752</th>\n",
              "      <th>g_753</th>\n",
              "      <th>g_754</th>\n",
              "      <th>g_755</th>\n",
              "      <th>g_756</th>\n",
              "      <th>g_757</th>\n",
              "      <th>g_758</th>\n",
              "      <th>g_759</th>\n",
              "      <th>g_760</th>\n",
              "      <th>g_761</th>\n",
              "      <th>g_762</th>\n",
              "      <th>g_763</th>\n",
              "      <th>g_764</th>\n",
              "      <th>g_765</th>\n",
              "      <th>g_766</th>\n",
              "      <th>g_767</th>\n",
              "      <th>g_768</th>\n",
              "      <th>g_769</th>\n",
              "      <th>g_770</th>\n",
              "      <th>g_771</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.248366</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>-0.138836</td>\n",
              "      <td>0.035961</td>\n",
              "      <td>-0.202651</td>\n",
              "      <td>-0.190083</td>\n",
              "      <td>0.119905</td>\n",
              "      <td>-0.123321</td>\n",
              "      <td>0.182307</td>\n",
              "      <td>0.143203</td>\n",
              "      <td>0.209402</td>\n",
              "      <td>-0.173884</td>\n",
              "      <td>-0.024432</td>\n",
              "      <td>0.126823</td>\n",
              "      <td>-0.146663</td>\n",
              "      <td>0.087687</td>\n",
              "      <td>-0.082982</td>\n",
              "      <td>-0.111908</td>\n",
              "      <td>-0.087379</td>\n",
              "      <td>0.047548</td>\n",
              "      <td>-0.117474</td>\n",
              "      <td>-0.113212</td>\n",
              "      <td>-0.052746</td>\n",
              "      <td>-0.091055</td>\n",
              "      <td>0.112176</td>\n",
              "      <td>-0.046458</td>\n",
              "      <td>-0.076239</td>\n",
              "      <td>-0.197699</td>\n",
              "      <td>0.382177</td>\n",
              "      <td>-0.189432</td>\n",
              "      <td>0.078791</td>\n",
              "      <td>-0.093312</td>\n",
              "      <td>0.135729</td>\n",
              "      <td>-0.188616</td>\n",
              "      <td>-0.606710</td>\n",
              "      <td>0.534425</td>\n",
              "      <td>0.132026</td>\n",
              "      <td>...</td>\n",
              "      <td>0.263569</td>\n",
              "      <td>-0.242638</td>\n",
              "      <td>0.110180</td>\n",
              "      <td>-0.039868</td>\n",
              "      <td>-0.064056</td>\n",
              "      <td>0.193629</td>\n",
              "      <td>0.004470</td>\n",
              "      <td>-0.187085</td>\n",
              "      <td>0.067186</td>\n",
              "      <td>0.006159</td>\n",
              "      <td>-0.201189</td>\n",
              "      <td>-0.243317</td>\n",
              "      <td>0.548903</td>\n",
              "      <td>0.247065</td>\n",
              "      <td>0.085950</td>\n",
              "      <td>0.090544</td>\n",
              "      <td>0.215525</td>\n",
              "      <td>-0.007705</td>\n",
              "      <td>0.236954</td>\n",
              "      <td>0.022785</td>\n",
              "      <td>0.103224</td>\n",
              "      <td>0.048402</td>\n",
              "      <td>0.109367</td>\n",
              "      <td>-0.103262</td>\n",
              "      <td>0.004129</td>\n",
              "      <td>0.066211</td>\n",
              "      <td>0.072773</td>\n",
              "      <td>-0.033051</td>\n",
              "      <td>-0.400643</td>\n",
              "      <td>-0.338978</td>\n",
              "      <td>0.207299</td>\n",
              "      <td>0.009161</td>\n",
              "      <td>0.171027</td>\n",
              "      <td>0.002316</td>\n",
              "      <td>-0.209799</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>0.101524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.393399</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>1.179388</td>\n",
              "      <td>0.882395</td>\n",
              "      <td>1.125494</td>\n",
              "      <td>1.749885</td>\n",
              "      <td>1.087180</td>\n",
              "      <td>1.291501</td>\n",
              "      <td>1.253604</td>\n",
              "      <td>1.234590</td>\n",
              "      <td>1.273068</td>\n",
              "      <td>1.247178</td>\n",
              "      <td>0.659839</td>\n",
              "      <td>1.418997</td>\n",
              "      <td>1.179688</td>\n",
              "      <td>0.743301</td>\n",
              "      <td>0.844796</td>\n",
              "      <td>1.219529</td>\n",
              "      <td>0.824401</td>\n",
              "      <td>0.924838</td>\n",
              "      <td>0.760159</td>\n",
              "      <td>1.203186</td>\n",
              "      <td>0.866977</td>\n",
              "      <td>1.103765</td>\n",
              "      <td>1.001687</td>\n",
              "      <td>1.027758</td>\n",
              "      <td>1.279399</td>\n",
              "      <td>1.302567</td>\n",
              "      <td>1.559174</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>1.172270</td>\n",
              "      <td>1.174325</td>\n",
              "      <td>1.061719</td>\n",
              "      <td>1.397677</td>\n",
              "      <td>2.200277</td>\n",
              "      <td>2.003317</td>\n",
              "      <td>0.990477</td>\n",
              "      <td>...</td>\n",
              "      <td>1.075809</td>\n",
              "      <td>1.309482</td>\n",
              "      <td>0.881283</td>\n",
              "      <td>0.917119</td>\n",
              "      <td>1.186966</td>\n",
              "      <td>1.212621</td>\n",
              "      <td>1.175611</td>\n",
              "      <td>1.182180</td>\n",
              "      <td>1.057292</td>\n",
              "      <td>0.996940</td>\n",
              "      <td>1.029370</td>\n",
              "      <td>1.378815</td>\n",
              "      <td>2.023791</td>\n",
              "      <td>1.639307</td>\n",
              "      <td>1.238753</td>\n",
              "      <td>0.961212</td>\n",
              "      <td>1.293783</td>\n",
              "      <td>0.966213</td>\n",
              "      <td>1.284074</td>\n",
              "      <td>1.015557</td>\n",
              "      <td>1.035968</td>\n",
              "      <td>1.048645</td>\n",
              "      <td>0.856080</td>\n",
              "      <td>1.175372</td>\n",
              "      <td>0.701031</td>\n",
              "      <td>0.996914</td>\n",
              "      <td>1.077310</td>\n",
              "      <td>1.579948</td>\n",
              "      <td>1.913468</td>\n",
              "      <td>1.701554</td>\n",
              "      <td>1.096488</td>\n",
              "      <td>1.200395</td>\n",
              "      <td>1.357701</td>\n",
              "      <td>1.052771</td>\n",
              "      <td>1.201918</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>1.417674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.513000</td>\n",
              "      <td>-5.737000</td>\n",
              "      <td>-9.104000</td>\n",
              "      <td>-5.998000</td>\n",
              "      <td>-6.369000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.337000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.870000</td>\n",
              "      <td>-8.587000</td>\n",
              "      <td>-5.018000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.226000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.700000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.272000</td>\n",
              "      <td>-8.184000</td>\n",
              "      <td>-4.835000</td>\n",
              "      <td>-7.913000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.956000</td>\n",
              "      <td>-8.356000</td>\n",
              "      <td>-7.182000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.261000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.223000</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.504000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-3.790000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-7.920000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.462000</td>\n",
              "      <td>-6.634000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.912000</td>\n",
              "      <td>-9.833000</td>\n",
              "      <td>-7.227000</td>\n",
              "      <td>-5.561000</td>\n",
              "      <td>-8.393000</td>\n",
              "      <td>-5.790000</td>\n",
              "      <td>-4.915000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-7.918000</td>\n",
              "      <td>-5.255000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-6.264000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.505000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.193000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.795000</td>\n",
              "      <td>-8.180000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.269000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.602225</td>\n",
              "      <td>-0.493900</td>\n",
              "      <td>-0.525175</td>\n",
              "      <td>-0.511675</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.511475</td>\n",
              "      <td>-0.489675</td>\n",
              "      <td>-0.447500</td>\n",
              "      <td>-0.481200</td>\n",
              "      <td>-0.607975</td>\n",
              "      <td>-0.404150</td>\n",
              "      <td>-0.391950</td>\n",
              "      <td>-0.513775</td>\n",
              "      <td>-0.272200</td>\n",
              "      <td>-0.488675</td>\n",
              "      <td>-0.524600</td>\n",
              "      <td>-0.538900</td>\n",
              "      <td>-0.440375</td>\n",
              "      <td>-0.508900</td>\n",
              "      <td>-0.533900</td>\n",
              "      <td>-0.497700</td>\n",
              "      <td>-0.512875</td>\n",
              "      <td>-0.467800</td>\n",
              "      <td>-0.378300</td>\n",
              "      <td>-0.505750</td>\n",
              "      <td>-0.457975</td>\n",
              "      <td>-0.328200</td>\n",
              "      <td>-0.600500</td>\n",
              "      <td>-0.478700</td>\n",
              "      <td>-0.570525</td>\n",
              "      <td>-0.481800</td>\n",
              "      <td>-0.541950</td>\n",
              "      <td>-0.604100</td>\n",
              "      <td>-0.470250</td>\n",
              "      <td>-0.401700</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.390700</td>\n",
              "      <td>-0.565075</td>\n",
              "      <td>-0.433275</td>\n",
              "      <td>-0.498650</td>\n",
              "      <td>-0.526300</td>\n",
              "      <td>-0.430750</td>\n",
              "      <td>-0.488775</td>\n",
              "      <td>-0.593125</td>\n",
              "      <td>-0.498275</td>\n",
              "      <td>-0.489800</td>\n",
              "      <td>-0.564100</td>\n",
              "      <td>-0.538000</td>\n",
              "      <td>-0.464000</td>\n",
              "      <td>-0.521975</td>\n",
              "      <td>-0.488600</td>\n",
              "      <td>-0.442925</td>\n",
              "      <td>-0.457200</td>\n",
              "      <td>-0.477900</td>\n",
              "      <td>-0.458175</td>\n",
              "      <td>-0.479075</td>\n",
              "      <td>-0.478825</td>\n",
              "      <td>-0.489300</td>\n",
              "      <td>-0.327500</td>\n",
              "      <td>-0.576775</td>\n",
              "      <td>-0.278275</td>\n",
              "      <td>-0.492700</td>\n",
              "      <td>-0.501575</td>\n",
              "      <td>-0.526450</td>\n",
              "      <td>-0.507375</td>\n",
              "      <td>-0.559175</td>\n",
              "      <td>-0.433875</td>\n",
              "      <td>-0.502250</td>\n",
              "      <td>-0.537075</td>\n",
              "      <td>-0.508775</td>\n",
              "      <td>-0.606175</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>-0.523800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.008850</td>\n",
              "      <td>-0.046600</td>\n",
              "      <td>0.075200</td>\n",
              "      <td>0.008050</td>\n",
              "      <td>-0.026900</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>-0.017900</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.160450</td>\n",
              "      <td>0.038550</td>\n",
              "      <td>0.013800</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>-0.030100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.149400</td>\n",
              "      <td>-0.002200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.027800</td>\n",
              "      <td>-0.002800</td>\n",
              "      <td>-0.069350</td>\n",
              "      <td>-0.011800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018300</td>\n",
              "      <td>-0.011650</td>\n",
              "      <td>0.016100</td>\n",
              "      <td>0.037600</td>\n",
              "      <td>0.003750</td>\n",
              "      <td>0.021750</td>\n",
              "      <td>0.027000</td>\n",
              "      <td>0.019100</td>\n",
              "      <td>-0.054000</td>\n",
              "      <td>0.023350</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>-0.000600</td>\n",
              "      <td>0.015350</td>\n",
              "      <td>-0.000700</td>\n",
              "      <td>0.005800</td>\n",
              "      <td>0.007400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.065650</td>\n",
              "      <td>-0.014100</td>\n",
              "      <td>0.030850</td>\n",
              "      <td>-0.001800</td>\n",
              "      <td>-0.008400</td>\n",
              "      <td>0.101400</td>\n",
              "      <td>0.033400</td>\n",
              "      <td>-0.001600</td>\n",
              "      <td>0.000850</td>\n",
              "      <td>0.014800</td>\n",
              "      <td>-0.044000</td>\n",
              "      <td>0.017400</td>\n",
              "      <td>0.000850</td>\n",
              "      <td>-0.015550</td>\n",
              "      <td>0.025350</td>\n",
              "      <td>0.017750</td>\n",
              "      <td>0.053450</td>\n",
              "      <td>0.027700</td>\n",
              "      <td>-0.002100</td>\n",
              "      <td>0.009500</td>\n",
              "      <td>-0.004450</td>\n",
              "      <td>-0.000050</td>\n",
              "      <td>0.002400</td>\n",
              "      <td>0.024250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005300</td>\n",
              "      <td>0.013600</td>\n",
              "      <td>0.001350</td>\n",
              "      <td>0.007350</td>\n",
              "      <td>0.038700</td>\n",
              "      <td>0.059600</td>\n",
              "      <td>-0.005900</td>\n",
              "      <td>-0.013600</td>\n",
              "      <td>-0.027700</td>\n",
              "      <td>-0.011850</td>\n",
              "      <td>0.009900</td>\n",
              "      <td>0.005400</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>0.028700</td>\n",
              "      <td>-0.006500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.525700</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>0.510425</td>\n",
              "      <td>0.528725</td>\n",
              "      <td>0.411900</td>\n",
              "      <td>0.549225</td>\n",
              "      <td>0.697775</td>\n",
              "      <td>0.525400</td>\n",
              "      <td>0.575275</td>\n",
              "      <td>0.604450</td>\n",
              "      <td>0.575825</td>\n",
              "      <td>0.457975</td>\n",
              "      <td>0.382475</td>\n",
              "      <td>0.829500</td>\n",
              "      <td>0.494775</td>\n",
              "      <td>0.327800</td>\n",
              "      <td>0.400600</td>\n",
              "      <td>0.492400</td>\n",
              "      <td>0.414875</td>\n",
              "      <td>0.433400</td>\n",
              "      <td>0.329250</td>\n",
              "      <td>0.527700</td>\n",
              "      <td>0.461650</td>\n",
              "      <td>0.508425</td>\n",
              "      <td>0.586450</td>\n",
              "      <td>0.431275</td>\n",
              "      <td>0.507600</td>\n",
              "      <td>0.458075</td>\n",
              "      <td>0.471075</td>\n",
              "      <td>0.391950</td>\n",
              "      <td>0.551300</td>\n",
              "      <td>0.503725</td>\n",
              "      <td>0.564875</td>\n",
              "      <td>0.517025</td>\n",
              "      <td>0.460500</td>\n",
              "      <td>0.642300</td>\n",
              "      <td>0.439400</td>\n",
              "      <td>...</td>\n",
              "      <td>0.646900</td>\n",
              "      <td>0.467900</td>\n",
              "      <td>0.543400</td>\n",
              "      <td>0.477200</td>\n",
              "      <td>0.497400</td>\n",
              "      <td>0.846400</td>\n",
              "      <td>0.573875</td>\n",
              "      <td>0.501675</td>\n",
              "      <td>0.519825</td>\n",
              "      <td>0.515000</td>\n",
              "      <td>0.412375</td>\n",
              "      <td>0.479400</td>\n",
              "      <td>0.646325</td>\n",
              "      <td>0.565200</td>\n",
              "      <td>0.610075</td>\n",
              "      <td>0.506975</td>\n",
              "      <td>0.671725</td>\n",
              "      <td>0.514175</td>\n",
              "      <td>0.524175</td>\n",
              "      <td>0.501325</td>\n",
              "      <td>0.516975</td>\n",
              "      <td>0.517475</td>\n",
              "      <td>0.375275</td>\n",
              "      <td>0.572250</td>\n",
              "      <td>0.251600</td>\n",
              "      <td>0.540950</td>\n",
              "      <td>0.530900</td>\n",
              "      <td>0.527900</td>\n",
              "      <td>0.475275</td>\n",
              "      <td>0.522075</td>\n",
              "      <td>0.648500</td>\n",
              "      <td>0.492150</td>\n",
              "      <td>0.522500</td>\n",
              "      <td>0.457000</td>\n",
              "      <td>0.483000</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>0.536950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.039000</td>\n",
              "      <td>8.257000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.282000</td>\n",
              "      <td>7.333000</td>\n",
              "      <td>5.473000</td>\n",
              "      <td>8.887000</td>\n",
              "      <td>6.433000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.134000</td>\n",
              "      <td>6.418000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>8.872000</td>\n",
              "      <td>4.081000</td>\n",
              "      <td>9.842000</td>\n",
              "      <td>5.248000</td>\n",
              "      <td>5.942000</td>\n",
              "      <td>5.201000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.494000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.416000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.796000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.834000</td>\n",
              "      <td>5.602000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.444000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.889000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.635000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.664000</td>\n",
              "      <td>9.566000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.497000</td>\n",
              "      <td>6.911000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.323000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.533000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.008000</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.552000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.346000</td>\n",
              "      <td>5.444000</td>\n",
              "      <td>6.317000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.911000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 772 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                g_0           g_1  ...         g_770         g_771\n",
              "count  23814.000000  23814.000000  ...  23814.000000  23814.000000\n",
              "mean       0.248366     -0.095684  ...     -0.219210      0.101524\n",
              "std        1.393399      0.812363  ...      1.326193      1.417674\n",
              "min       -5.513000     -5.737000  ...    -10.000000    -10.000000\n",
              "25%       -0.473075     -0.562200  ...     -0.554400     -0.523800\n",
              "50%       -0.008850     -0.046600  ...      0.028700     -0.006500\n",
              "75%        0.525700      0.403075  ...      0.496400      0.536950\n",
              "max       10.000000      5.039000  ...     10.000000     10.000000\n",
              "\n",
              "[8 rows x 772 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Bd8_gRdyItw"
      },
      "source": [
        "As informações podem ser invertidas, ou seja os valores passam para linhas, dessa forma é possível fazer uma visualização de todas as médias, por exemplo. Aqui eu optei por colocar esses dados em uma variável para facilitar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "pU48DWuqcJml",
        "outputId": "a02618b3-d1fc-4bb3-9217-77509cd15bf1"
      },
      "source": [
        "transposta_g = dados.loc[:,'g_0':'g_771'].describe().T\n",
        "transposta_g"
      ],
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g_0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.00885</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>-5.737</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.04660</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>0.07520</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>8.257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>-5.998</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>0.00805</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.02690</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_767</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>0.00990</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>6.317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_768</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>-4.269</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>0.00540</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_769</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>0.00060</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>5.911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_770</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_771</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>1.417674</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.523800</td>\n",
              "      <td>-0.00650</td>\n",
              "      <td>0.536950</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>772 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean       std     min       25%      50%       75%     max\n",
              "g_0    23814.0  0.248366  1.393399  -5.513 -0.473075 -0.00885  0.525700  10.000\n",
              "g_1    23814.0 -0.095684  0.812363  -5.737 -0.562200 -0.04660  0.403075   5.039\n",
              "g_2    23814.0  0.152253  1.035731  -9.104 -0.437750  0.07520  0.663925   8.257\n",
              "g_3    23814.0  0.081971  0.950012  -5.998 -0.429575  0.00805  0.463400  10.000\n",
              "g_4    23814.0  0.057347  1.032091  -6.369 -0.470925 -0.02690  0.465375  10.000\n",
              "...        ...       ...       ...     ...       ...      ...       ...     ...\n",
              "g_767  23814.0 -0.076251  1.115477 -10.000 -0.506200  0.00990  0.511175   6.317\n",
              "g_768  23814.0  0.134162  0.951264  -4.269 -0.353100  0.00540  0.409075  10.000\n",
              "g_769  23814.0 -0.128018  1.230636 -10.000 -0.544600  0.00060  0.498500   5.911\n",
              "g_770  23814.0 -0.219210  1.326193 -10.000 -0.554400  0.02870  0.496400  10.000\n",
              "g_771  23814.0  0.101524  1.417674 -10.000 -0.523800 -0.00650  0.536950  10.000\n",
              "\n",
              "[772 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fc48I-OzJ3E"
      },
      "source": [
        "Gráfico apresentado em aula."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Z5D1g8aUy5Cd",
        "outputId": "6167b474-ded5-47c5-b2fe-0aabc36379e4"
      },
      "source": [
        "dados['g_0'].hist(bins = 50)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3fc6b01050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IM_jeclF4JjY"
      },
      "source": [
        "####Antes e Depois\n",
        "Comparando dois gráficos lado a lado. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "zB9R20qo2YWw",
        "outputId": "b73a7318-983a-40c0-d211-59097a448a1a"
      },
      "source": [
        "# Colocando dois gráficos lado a lado\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
        "plt.suptitle('Distribuição das expressões gênicas do Gene 0 \\n Histograma')\n",
        "\n",
        "# histograma da aula à esquerda\n",
        "axes[0].hist(x=dados['g_0'], bins = 50)\n",
        "axes[0].set_title('Pandas', fontdict={'fontsize': 13})\n",
        "\n",
        "# Minha sugestão, utilizando o Seaborn\n",
        "axes[1]=sns.histplot(data=dados, x='g_0', kde=True)\n",
        "axes[1].set_title('Biblioteca Seaborn')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ErbVBh89WuC"
      },
      "source": [
        "Interessante perceber que a função histplot já plota o histograma com uma quantidade maior de bins sem precisarmos passar tal informação para a função."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "vSkzAE0e0O0E",
        "outputId": "a276632c-b89e-4973-9908-49db765160d5"
      },
      "source": [
        "# Deixando o gráfico mais bonitinho.\n",
        "plt.figure(figsize=(10,6))\n",
        "axes[1]=sns.histplot(data=dados, x='g_0', kde=True, color= 'green')\n",
        "axes[1].set_title('Experimentos Gene 0', size=13, weight='bold')\n",
        "axes[1].set_xlabel('Gene 0', size=12)\n",
        "axes[1].set_ylabel('Total de amostras', size=12)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YubYRkqqB1mF"
      },
      "source": [
        "####Desafio 04\n",
        "Estudar sobre as estatísticas retornadas no .describe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "MY8gRU6jCC5u",
        "outputId": "5e1892a7-9034-4092-bf23-7010e6ca27de"
      },
      "source": [
        "dados.describe()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tempo</th>\n",
              "      <th>g_0</th>\n",
              "      <th>g_1</th>\n",
              "      <th>g_2</th>\n",
              "      <th>g_3</th>\n",
              "      <th>g_4</th>\n",
              "      <th>g_5</th>\n",
              "      <th>g_6</th>\n",
              "      <th>g_7</th>\n",
              "      <th>g_8</th>\n",
              "      <th>g_9</th>\n",
              "      <th>g_10</th>\n",
              "      <th>g_11</th>\n",
              "      <th>g_12</th>\n",
              "      <th>g_13</th>\n",
              "      <th>g_14</th>\n",
              "      <th>g_15</th>\n",
              "      <th>g_16</th>\n",
              "      <th>g_17</th>\n",
              "      <th>g_18</th>\n",
              "      <th>g_19</th>\n",
              "      <th>g_20</th>\n",
              "      <th>g_21</th>\n",
              "      <th>g_22</th>\n",
              "      <th>g_23</th>\n",
              "      <th>g_24</th>\n",
              "      <th>g_25</th>\n",
              "      <th>g_26</th>\n",
              "      <th>g_27</th>\n",
              "      <th>g_28</th>\n",
              "      <th>g_29</th>\n",
              "      <th>g_30</th>\n",
              "      <th>g_31</th>\n",
              "      <th>g_32</th>\n",
              "      <th>g_33</th>\n",
              "      <th>g_34</th>\n",
              "      <th>g_35</th>\n",
              "      <th>g_36</th>\n",
              "      <th>g_37</th>\n",
              "      <th>g_38</th>\n",
              "      <th>...</th>\n",
              "      <th>c_60</th>\n",
              "      <th>c_61</th>\n",
              "      <th>c_62</th>\n",
              "      <th>c_63</th>\n",
              "      <th>c_64</th>\n",
              "      <th>c_65</th>\n",
              "      <th>c_66</th>\n",
              "      <th>c_67</th>\n",
              "      <th>c_68</th>\n",
              "      <th>c_69</th>\n",
              "      <th>c_70</th>\n",
              "      <th>c_71</th>\n",
              "      <th>c_72</th>\n",
              "      <th>c_73</th>\n",
              "      <th>c_74</th>\n",
              "      <th>c_75</th>\n",
              "      <th>c_76</th>\n",
              "      <th>c_77</th>\n",
              "      <th>c_78</th>\n",
              "      <th>c_79</th>\n",
              "      <th>c_80</th>\n",
              "      <th>c_81</th>\n",
              "      <th>c_82</th>\n",
              "      <th>c_83</th>\n",
              "      <th>c_84</th>\n",
              "      <th>c_85</th>\n",
              "      <th>c_86</th>\n",
              "      <th>c_87</th>\n",
              "      <th>c_88</th>\n",
              "      <th>c_89</th>\n",
              "      <th>c_90</th>\n",
              "      <th>c_91</th>\n",
              "      <th>c_92</th>\n",
              "      <th>c_93</th>\n",
              "      <th>c_94</th>\n",
              "      <th>c_95</th>\n",
              "      <th>c_96</th>\n",
              "      <th>c_97</th>\n",
              "      <th>c_98</th>\n",
              "      <th>c_99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "      <td>23814.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>48.020156</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>-0.138836</td>\n",
              "      <td>0.035961</td>\n",
              "      <td>-0.202651</td>\n",
              "      <td>-0.190083</td>\n",
              "      <td>0.119905</td>\n",
              "      <td>-0.123321</td>\n",
              "      <td>0.182307</td>\n",
              "      <td>0.143203</td>\n",
              "      <td>0.209402</td>\n",
              "      <td>-0.173884</td>\n",
              "      <td>-0.024432</td>\n",
              "      <td>0.126823</td>\n",
              "      <td>-0.146663</td>\n",
              "      <td>0.087687</td>\n",
              "      <td>-0.082982</td>\n",
              "      <td>-0.111908</td>\n",
              "      <td>-0.087379</td>\n",
              "      <td>0.047548</td>\n",
              "      <td>-0.117474</td>\n",
              "      <td>-0.113212</td>\n",
              "      <td>-0.052746</td>\n",
              "      <td>-0.091055</td>\n",
              "      <td>0.112176</td>\n",
              "      <td>-0.046458</td>\n",
              "      <td>-0.076239</td>\n",
              "      <td>-0.197699</td>\n",
              "      <td>0.382177</td>\n",
              "      <td>-0.189432</td>\n",
              "      <td>0.078791</td>\n",
              "      <td>-0.093312</td>\n",
              "      <td>0.135729</td>\n",
              "      <td>-0.188616</td>\n",
              "      <td>-0.606710</td>\n",
              "      <td>0.534425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.517397</td>\n",
              "      <td>-0.360770</td>\n",
              "      <td>-0.435752</td>\n",
              "      <td>-0.613591</td>\n",
              "      <td>-0.402083</td>\n",
              "      <td>-0.619682</td>\n",
              "      <td>-0.452265</td>\n",
              "      <td>-0.497164</td>\n",
              "      <td>-0.413836</td>\n",
              "      <td>-0.277029</td>\n",
              "      <td>-0.547845</td>\n",
              "      <td>-0.358611</td>\n",
              "      <td>-0.442906</td>\n",
              "      <td>-0.475194</td>\n",
              "      <td>-0.010404</td>\n",
              "      <td>-0.467001</td>\n",
              "      <td>-0.276963</td>\n",
              "      <td>-0.455848</td>\n",
              "      <td>-0.412918</td>\n",
              "      <td>-0.456404</td>\n",
              "      <td>-0.472514</td>\n",
              "      <td>-0.505481</td>\n",
              "      <td>-0.492735</td>\n",
              "      <td>-0.446836</td>\n",
              "      <td>-0.463029</td>\n",
              "      <td>-0.409310</td>\n",
              "      <td>-0.333124</td>\n",
              "      <td>-0.295009</td>\n",
              "      <td>-0.328342</td>\n",
              "      <td>-0.401615</td>\n",
              "      <td>-0.469244</td>\n",
              "      <td>-0.461411</td>\n",
              "      <td>-0.513256</td>\n",
              "      <td>-0.500142</td>\n",
              "      <td>-0.507093</td>\n",
              "      <td>-0.353726</td>\n",
              "      <td>-0.463485</td>\n",
              "      <td>-0.378241</td>\n",
              "      <td>-0.470252</td>\n",
              "      <td>-0.301505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19.402807</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>1.179388</td>\n",
              "      <td>0.882395</td>\n",
              "      <td>1.125494</td>\n",
              "      <td>1.749885</td>\n",
              "      <td>1.087180</td>\n",
              "      <td>1.291501</td>\n",
              "      <td>1.253604</td>\n",
              "      <td>1.234590</td>\n",
              "      <td>1.273068</td>\n",
              "      <td>1.247178</td>\n",
              "      <td>0.659839</td>\n",
              "      <td>1.418997</td>\n",
              "      <td>1.179688</td>\n",
              "      <td>0.743301</td>\n",
              "      <td>0.844796</td>\n",
              "      <td>1.219529</td>\n",
              "      <td>0.824401</td>\n",
              "      <td>0.924838</td>\n",
              "      <td>0.760159</td>\n",
              "      <td>1.203186</td>\n",
              "      <td>0.866977</td>\n",
              "      <td>1.103765</td>\n",
              "      <td>1.001687</td>\n",
              "      <td>1.027758</td>\n",
              "      <td>1.279399</td>\n",
              "      <td>1.302567</td>\n",
              "      <td>1.559174</td>\n",
              "      <td>0.933514</td>\n",
              "      <td>1.172270</td>\n",
              "      <td>1.174325</td>\n",
              "      <td>1.061719</td>\n",
              "      <td>1.397677</td>\n",
              "      <td>2.200277</td>\n",
              "      <td>2.003317</td>\n",
              "      <td>...</td>\n",
              "      <td>2.122318</td>\n",
              "      <td>1.710725</td>\n",
              "      <td>1.898871</td>\n",
              "      <td>2.307820</td>\n",
              "      <td>1.785055</td>\n",
              "      <td>2.225596</td>\n",
              "      <td>1.991021</td>\n",
              "      <td>2.063896</td>\n",
              "      <td>1.887001</td>\n",
              "      <td>1.459639</td>\n",
              "      <td>2.187835</td>\n",
              "      <td>1.730634</td>\n",
              "      <td>1.924716</td>\n",
              "      <td>2.021927</td>\n",
              "      <td>1.029820</td>\n",
              "      <td>2.004317</td>\n",
              "      <td>1.429340</td>\n",
              "      <td>1.924263</td>\n",
              "      <td>1.888788</td>\n",
              "      <td>1.832863</td>\n",
              "      <td>2.011396</td>\n",
              "      <td>2.091353</td>\n",
              "      <td>2.055624</td>\n",
              "      <td>1.987476</td>\n",
              "      <td>2.014045</td>\n",
              "      <td>1.883974</td>\n",
              "      <td>1.647241</td>\n",
              "      <td>1.634073</td>\n",
              "      <td>1.663170</td>\n",
              "      <td>1.832794</td>\n",
              "      <td>2.000488</td>\n",
              "      <td>2.042475</td>\n",
              "      <td>2.001714</td>\n",
              "      <td>2.107105</td>\n",
              "      <td>2.159589</td>\n",
              "      <td>1.629291</td>\n",
              "      <td>2.059725</td>\n",
              "      <td>1.703615</td>\n",
              "      <td>1.834828</td>\n",
              "      <td>1.407918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>-5.513000</td>\n",
              "      <td>-5.737000</td>\n",
              "      <td>-9.104000</td>\n",
              "      <td>-5.998000</td>\n",
              "      <td>-6.369000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.337000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.870000</td>\n",
              "      <td>-8.587000</td>\n",
              "      <td>-5.018000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-4.226000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-5.700000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-8.272000</td>\n",
              "      <td>-8.184000</td>\n",
              "      <td>-4.835000</td>\n",
              "      <td>-7.913000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-2.956000</td>\n",
              "      <td>-8.356000</td>\n",
              "      <td>-7.182000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.261000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.839000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-6.452000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-9.938000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "      <td>-10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.602225</td>\n",
              "      <td>-0.493900</td>\n",
              "      <td>-0.525175</td>\n",
              "      <td>-0.511675</td>\n",
              "      <td>-0.360200</td>\n",
              "      <td>-0.511475</td>\n",
              "      <td>-0.489675</td>\n",
              "      <td>-0.447500</td>\n",
              "      <td>-0.481200</td>\n",
              "      <td>-0.607975</td>\n",
              "      <td>-0.404150</td>\n",
              "      <td>-0.391950</td>\n",
              "      <td>-0.513775</td>\n",
              "      <td>-0.272200</td>\n",
              "      <td>-0.488675</td>\n",
              "      <td>-0.524600</td>\n",
              "      <td>-0.538900</td>\n",
              "      <td>-0.440375</td>\n",
              "      <td>-0.508900</td>\n",
              "      <td>-0.533900</td>\n",
              "      <td>-0.497700</td>\n",
              "      <td>-0.512875</td>\n",
              "      <td>-0.467800</td>\n",
              "      <td>-0.378300</td>\n",
              "      <td>-0.505750</td>\n",
              "      <td>-0.457975</td>\n",
              "      <td>-0.328200</td>\n",
              "      <td>-0.600500</td>\n",
              "      <td>-0.478700</td>\n",
              "      <td>-0.570525</td>\n",
              "      <td>-0.481800</td>\n",
              "      <td>-0.541950</td>\n",
              "      <td>-0.604100</td>\n",
              "      <td>-0.470250</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.588075</td>\n",
              "      <td>-0.564025</td>\n",
              "      <td>-0.561000</td>\n",
              "      <td>-0.583250</td>\n",
              "      <td>-0.566500</td>\n",
              "      <td>-0.603200</td>\n",
              "      <td>-0.541575</td>\n",
              "      <td>-0.560825</td>\n",
              "      <td>-0.555200</td>\n",
              "      <td>-0.534500</td>\n",
              "      <td>-0.569100</td>\n",
              "      <td>-0.558300</td>\n",
              "      <td>-0.573350</td>\n",
              "      <td>-0.594275</td>\n",
              "      <td>-0.389925</td>\n",
              "      <td>-0.551200</td>\n",
              "      <td>-0.544150</td>\n",
              "      <td>-0.575075</td>\n",
              "      <td>-0.568275</td>\n",
              "      <td>-0.582650</td>\n",
              "      <td>-0.558575</td>\n",
              "      <td>-0.562375</td>\n",
              "      <td>-0.572800</td>\n",
              "      <td>-0.561225</td>\n",
              "      <td>-0.560675</td>\n",
              "      <td>-0.560100</td>\n",
              "      <td>-0.533700</td>\n",
              "      <td>-0.504575</td>\n",
              "      <td>-0.544275</td>\n",
              "      <td>-0.569150</td>\n",
              "      <td>-0.566175</td>\n",
              "      <td>-0.565975</td>\n",
              "      <td>-0.589975</td>\n",
              "      <td>-0.568700</td>\n",
              "      <td>-0.563775</td>\n",
              "      <td>-0.567975</td>\n",
              "      <td>-0.552575</td>\n",
              "      <td>-0.561000</td>\n",
              "      <td>-0.592600</td>\n",
              "      <td>-0.562900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>-0.008850</td>\n",
              "      <td>-0.046600</td>\n",
              "      <td>0.075200</td>\n",
              "      <td>0.008050</td>\n",
              "      <td>-0.026900</td>\n",
              "      <td>-0.015650</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>-0.017900</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.160450</td>\n",
              "      <td>0.038550</td>\n",
              "      <td>0.013800</td>\n",
              "      <td>0.060250</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>-0.030100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.149400</td>\n",
              "      <td>-0.002200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.027800</td>\n",
              "      <td>-0.002800</td>\n",
              "      <td>-0.069350</td>\n",
              "      <td>-0.011800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018300</td>\n",
              "      <td>-0.011650</td>\n",
              "      <td>0.016100</td>\n",
              "      <td>0.037600</td>\n",
              "      <td>0.003750</td>\n",
              "      <td>0.021750</td>\n",
              "      <td>0.027000</td>\n",
              "      <td>0.019100</td>\n",
              "      <td>-0.054000</td>\n",
              "      <td>0.023350</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>-0.000600</td>\n",
              "      <td>0.015350</td>\n",
              "      <td>-0.000700</td>\n",
              "      <td>0.005800</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.017650</td>\n",
              "      <td>-0.041550</td>\n",
              "      <td>-0.002950</td>\n",
              "      <td>-0.012650</td>\n",
              "      <td>-0.005600</td>\n",
              "      <td>0.007650</td>\n",
              "      <td>0.004950</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.023800</td>\n",
              "      <td>-0.011450</td>\n",
              "      <td>-0.007100</td>\n",
              "      <td>-0.019500</td>\n",
              "      <td>-0.019500</td>\n",
              "      <td>-0.009300</td>\n",
              "      <td>0.081550</td>\n",
              "      <td>-0.006900</td>\n",
              "      <td>0.018400</td>\n",
              "      <td>-0.014650</td>\n",
              "      <td>-0.014350</td>\n",
              "      <td>0.005300</td>\n",
              "      <td>-0.005300</td>\n",
              "      <td>-0.004050</td>\n",
              "      <td>0.003300</td>\n",
              "      <td>-0.007900</td>\n",
              "      <td>-0.004600</td>\n",
              "      <td>-0.002400</td>\n",
              "      <td>0.007850</td>\n",
              "      <td>-0.005600</td>\n",
              "      <td>-0.020600</td>\n",
              "      <td>-0.030000</td>\n",
              "      <td>-0.009900</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>-0.009100</td>\n",
              "      <td>-0.013750</td>\n",
              "      <td>-0.003300</td>\n",
              "      <td>-0.010250</td>\n",
              "      <td>-0.001250</td>\n",
              "      <td>-0.006800</td>\n",
              "      <td>0.014000</td>\n",
              "      <td>-0.019500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>0.510425</td>\n",
              "      <td>0.528725</td>\n",
              "      <td>0.411900</td>\n",
              "      <td>0.549225</td>\n",
              "      <td>0.697775</td>\n",
              "      <td>0.525400</td>\n",
              "      <td>0.575275</td>\n",
              "      <td>0.604450</td>\n",
              "      <td>0.575825</td>\n",
              "      <td>0.457975</td>\n",
              "      <td>0.382475</td>\n",
              "      <td>0.829500</td>\n",
              "      <td>0.494775</td>\n",
              "      <td>0.327800</td>\n",
              "      <td>0.400600</td>\n",
              "      <td>0.492400</td>\n",
              "      <td>0.414875</td>\n",
              "      <td>0.433400</td>\n",
              "      <td>0.329250</td>\n",
              "      <td>0.527700</td>\n",
              "      <td>0.461650</td>\n",
              "      <td>0.508425</td>\n",
              "      <td>0.586450</td>\n",
              "      <td>0.431275</td>\n",
              "      <td>0.507600</td>\n",
              "      <td>0.458075</td>\n",
              "      <td>0.471075</td>\n",
              "      <td>0.391950</td>\n",
              "      <td>0.551300</td>\n",
              "      <td>0.503725</td>\n",
              "      <td>0.564875</td>\n",
              "      <td>0.517025</td>\n",
              "      <td>0.460500</td>\n",
              "      <td>0.642300</td>\n",
              "      <td>...</td>\n",
              "      <td>0.452675</td>\n",
              "      <td>0.427675</td>\n",
              "      <td>0.462175</td>\n",
              "      <td>0.447975</td>\n",
              "      <td>0.447150</td>\n",
              "      <td>0.441250</td>\n",
              "      <td>0.470600</td>\n",
              "      <td>0.458550</td>\n",
              "      <td>0.441000</td>\n",
              "      <td>0.460075</td>\n",
              "      <td>0.460950</td>\n",
              "      <td>0.449975</td>\n",
              "      <td>0.445200</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.563575</td>\n",
              "      <td>0.456350</td>\n",
              "      <td>0.493400</td>\n",
              "      <td>0.448375</td>\n",
              "      <td>0.451975</td>\n",
              "      <td>0.463075</td>\n",
              "      <td>0.447675</td>\n",
              "      <td>0.462000</td>\n",
              "      <td>0.468900</td>\n",
              "      <td>0.452375</td>\n",
              "      <td>0.460475</td>\n",
              "      <td>0.461675</td>\n",
              "      <td>0.465950</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>0.450075</td>\n",
              "      <td>0.430875</td>\n",
              "      <td>0.457750</td>\n",
              "      <td>0.461500</td>\n",
              "      <td>0.445675</td>\n",
              "      <td>0.452900</td>\n",
              "      <td>0.470900</td>\n",
              "      <td>0.444750</td>\n",
              "      <td>0.465225</td>\n",
              "      <td>0.446400</td>\n",
              "      <td>0.461275</td>\n",
              "      <td>0.438650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.039000</td>\n",
              "      <td>8.257000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.282000</td>\n",
              "      <td>7.333000</td>\n",
              "      <td>5.473000</td>\n",
              "      <td>8.887000</td>\n",
              "      <td>6.433000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.134000</td>\n",
              "      <td>6.418000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>8.872000</td>\n",
              "      <td>4.081000</td>\n",
              "      <td>9.842000</td>\n",
              "      <td>5.248000</td>\n",
              "      <td>5.942000</td>\n",
              "      <td>5.201000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.494000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.416000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.796000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.834000</td>\n",
              "      <td>5.602000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.888000</td>\n",
              "      <td>3.596000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>3.549000</td>\n",
              "      <td>3.382000</td>\n",
              "      <td>3.328000</td>\n",
              "      <td>4.157000</td>\n",
              "      <td>3.736000</td>\n",
              "      <td>3.582000</td>\n",
              "      <td>3.119000</td>\n",
              "      <td>3.323000</td>\n",
              "      <td>5.014000</td>\n",
              "      <td>2.898000</td>\n",
              "      <td>4.185000</td>\n",
              "      <td>3.170000</td>\n",
              "      <td>3.276000</td>\n",
              "      <td>4.992000</td>\n",
              "      <td>3.770000</td>\n",
              "      <td>2.851000</td>\n",
              "      <td>3.211000</td>\n",
              "      <td>4.534000</td>\n",
              "      <td>3.890000</td>\n",
              "      <td>3.994000</td>\n",
              "      <td>4.321000</td>\n",
              "      <td>4.020000</td>\n",
              "      <td>3.738000</td>\n",
              "      <td>3.252000</td>\n",
              "      <td>5.406000</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>3.320000</td>\n",
              "      <td>4.069000</td>\n",
              "      <td>3.960000</td>\n",
              "      <td>3.927000</td>\n",
              "      <td>3.596000</td>\n",
              "      <td>3.747000</td>\n",
              "      <td>2.814000</td>\n",
              "      <td>3.505000</td>\n",
              "      <td>2.924000</td>\n",
              "      <td>3.111000</td>\n",
              "      <td>3.805000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 873 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              tempo           g_0  ...          c_98          c_99\n",
              "count  23814.000000  23814.000000  ...  23814.000000  23814.000000\n",
              "mean      48.020156      0.248366  ...     -0.470252     -0.301505\n",
              "std       19.402807      1.393399  ...      1.834828      1.407918\n",
              "min       24.000000     -5.513000  ...    -10.000000    -10.000000\n",
              "25%       24.000000     -0.473075  ...     -0.592600     -0.562900\n",
              "50%       48.000000     -0.008850  ...      0.014000     -0.019500\n",
              "75%       72.000000      0.525700  ...      0.461275      0.438650\n",
              "max       72.000000     10.000000  ...      3.111000      3.805000\n",
              "\n",
              "[8 rows x 873 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yArb7z1-CzSn"
      },
      "source": [
        "O describe traz de forma resumida os dados estatísticos principais, apenas de colunas ou variáveis do tipo *int* ou *float*, não funciona com *strings*. São eles contagem total, média, Desvio Padrão, mínimo, 1° Quartil, Mediana, 3°Quartil e máximo, nesta ordem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jpl4CsmDDjMF"
      },
      "source": [
        "***Count***/*Contagem* = retorna o número de linhas da coluna (incluso célula em branco);\n",
        "\n",
        "***Mean***/*Média*: retorna o valor da média aritmética da coluna;\n",
        "\n",
        "***std*** /*Desvio Padrão*: retorna o valor do desvio padrão;\n",
        "\n",
        "***min***: retorna o valor mínimo encontrado na coluna;\n",
        "\n",
        "- *Quartil é qualquer um dos três valores que divide o conjunto ordenado de dados em quatro partes iguais.*\n",
        "\n",
        "***25%*** /*1°quartil* /quartil inferior = é o valor no qual se encontram os 25% iniciais dos dados da coluna/amostra;\n",
        "\n",
        "***50%*** / *Mediana*: é o valor até o qual se encontra 50% da amostra ordenada. Ou seja divide a coluna/amostra em dois.\n",
        "\n",
        "***75%*** / *3°quartil*/ *quartil superior* = é o valor aos 75% da amostra ordenada. Ou o valor a partir do qual se encontram 25% dos valores mais elevados da coluna.\n",
        "\n",
        "**max**: retorna o valor máximo encontrado na coluna."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyKtWu1CMw_H"
      },
      "source": [
        "####Desafio 05\n",
        "Refletir sobre a manipulação dos tamanho das visualizações. Distorções o que são?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1u7dEgBR8gh"
      },
      "source": [
        "A visualização de dados transforma informações importantes de um conjunto de dados complicado em algo mais simples e de fácil entendimento, de forma gráfica. \n",
        "\n",
        "Os dados podem ser importantes, mas se não forem transmitidos da forma adequada, podem passar falsas impressões levando a tomada de decisões equivocadas. Atualmente existem várias empresas e livros que nos auxiliam na geração de infográficos.\n",
        "\n",
        "Um exemplo é a [Infogram](https://infogram.com/pt/pagina/visualizacao-de-dados), que é uma plataforma, que dá dicas de  como construir os melhores gráficos. \n",
        "\n",
        "*Para criar boas visualizações de Dados você precisa começar com dados limpos, bem fornecidos e completos. Quando seus dados estiverem prontos para visualizar, você precisará escolher o gráfico correto. Depois de ter decidido qual tipo de gráfico é melhor, você precisa projetar e personalizar sua visualização ao seu gosto. Lembre-se de que a simplicidade é fundamental.*\n",
        "\n",
        "Os dados contam uma história e por isso devem adequar-se ao público alvo, já que as pessoas utilizam os seus conhecimentos prévios pra interpretar a realidade. E para cada objetivo e público há um tipo de gráfico mais adequado, com tamanho e cores específicos. O não cumprimento destes requisitos pode gerar distorções nas informações apresentadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Py4qb3NidN7C"
      },
      "source": [
        "####Desafio 06\n",
        "Fazer outras análises com o boxplot e até com o histograma.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "c5EBKLzOB9wP",
        "outputId": "a62d3835-ebe8-4d0e-f4a0-6ea7f5443ab5"
      },
      "source": [
        "transposta_g"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g_0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.00885</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>-5.737</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.04660</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>0.07520</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>8.257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>-5.998</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>0.00805</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.02690</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_767</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>0.00990</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>6.317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_768</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>-4.269</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>0.00540</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_769</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>0.00060</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>5.911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_770</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_771</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>1.417674</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.523800</td>\n",
              "      <td>-0.00650</td>\n",
              "      <td>0.536950</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>772 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean       std     min       25%      50%       75%     max\n",
              "g_0    23814.0  0.248366  1.393399  -5.513 -0.473075 -0.00885  0.525700  10.000\n",
              "g_1    23814.0 -0.095684  0.812363  -5.737 -0.562200 -0.04660  0.403075   5.039\n",
              "g_2    23814.0  0.152253  1.035731  -9.104 -0.437750  0.07520  0.663925   8.257\n",
              "g_3    23814.0  0.081971  0.950012  -5.998 -0.429575  0.00805  0.463400  10.000\n",
              "g_4    23814.0  0.057347  1.032091  -6.369 -0.470925 -0.02690  0.465375  10.000\n",
              "...        ...       ...       ...     ...       ...      ...       ...     ...\n",
              "g_767  23814.0 -0.076251  1.115477 -10.000 -0.506200  0.00990  0.511175   6.317\n",
              "g_768  23814.0  0.134162  0.951264  -4.269 -0.353100  0.00540  0.409075  10.000\n",
              "g_769  23814.0 -0.128018  1.230636 -10.000 -0.544600  0.00060  0.498500   5.911\n",
              "g_770  23814.0 -0.219210  1.326193 -10.000 -0.554400  0.02870  0.496400  10.000\n",
              "g_771  23814.0  0.101524  1.417674 -10.000 -0.523800 -0.00650  0.536950  10.000\n",
              "\n",
              "[772 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irU09ZIbHV5O"
      },
      "source": [
        "Dos dados estatísticos optei por analisar um pouco mais sobre o desvio padrão. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zf6VcA6rEzzq"
      },
      "source": [
        "### O que é desvio padrão?\n",
        "- O desvio padrão é uma medida que indica a dispersão dos dados dentro de uma amostra com relação à média. Assim, quando se calcula o desvio padrão juntamente com a média de diferentes grupos, obtém-se mais informações para avaliar e diferenciar seus comportamentos.\n",
        "\n",
        "\n",
        "Referências: [Blog Oper - O que é desvio padrao e erro padrao](https://operdata.com.br/blog/desvio-padrao-e-erro-padrao/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnA7eVgOhcxG"
      },
      "source": [
        "####Utilizando o boxplot para mostrar como se comportam os desvios padrão de todos os genes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "A7iS4SqHhqF0",
        "outputId": "2dc4f8e9-d3d8-4601-fe6b-5f09cf94fc28"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "ax1 = sns.boxplot(x='std',data=transposta_g)\n",
        "ax1.set_title('Desvio padrão dos genes g_*', size=15, y=1.1)\n",
        "ax1.set_xlabel('Valores do desvio padrão')\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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46NChQ9qzZ4/Gjh1b6P6HDBmiTz/9VM8884yGDBmixMREvfvuuypfvrw1CDo6OmrSpEmaMGGCLly4oIcfflguLi46ceKEoqKitHDhQus7zq6mYsWKGjlypF577TXFxcWpdevWysnJ0fHjx7V7926Fh4fr8OHDmjt3rrp166batWvr3Llzevfdd9WoUaNCZ968vb3VoUMHvfrqq7p48aI8PT21fPlylStXrtB3SwKlBcEIpcr58+fVv39/OTg4qEKFCqpTp4569eqlQYMGWS/vSFcuD7399ttavHixPvzwQyUkJKhSpUpq1KiRQkNDJUk+Pj769ttv9frrr+vs2bPy9PTUY489lu8D7jp16qSpU6cqJSVF3bt3v2qNlj/kK1asUFJSkurUqaN58+YVep9Jbj169JCbm5umTJmitLQ0a8CxGDBggP73v//po48+UkZGhgIDA/Wvf/1Ljz/+uE0/s2fP1rRp0zRr1iy5urpq4MCB8vX11ddff33VGtq3b69x48bp3//+t1auXKkWLVpoyZIl6tKlyw05zgkTJigrK0vh4eFydHRUr169NHToUM2ePdvapkyZMvrggw/0+uuva8qUKZIkf39/hYWFWcOCr6+vPvjgA61fv14XL15UjRo19MILL2jw4MGF7rtatWpasmSJXnvtNY0cOVINGjTQrFmzNHToUJvZm+7du8vNzU1LlizRl19+KUdHR9WuXVvBwcFycXG56hjmNmzYMHl5eenDDz/U8uXLVbZsWdWrV896Lnl6eqpKlSpavHixTp8+rYoVKyogIKDQy4EWluf4tddesz7HtWvXtrlRHyiNHIzcb8UBcMfq2LGjunTpYvM5Obj5oqOj9fe//10ffvih2rRpU9LlXLOsrCz17NlTzZs315w5c0q6HKDEMGMEAMUwb948NWnSRFWrVtWxY8f0zjvvyMfH5477UMPIyEidPn1a3t7eunjxor744gvFxMRo7ty5JV0aUKIIRgBQDJmZmZo7d66SkpLk5uamwMBATZo0qcB3f93OXF1d9dVXXyk2NlbZ2dny9vbW4sWL9cADD0i6cj9eYRcUHBwcbul3CgK3EpfSAAD5dOzYMd/HLFjUrFmTzy7CXYtgBADI58iRI4V+GniZMmXyfdAlcLcgGAEAAJjurIviAAAANxHBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADA536iOUlIuysPDTUlJF25Ul3e9KlUqMF52YqyKh/GyH2NVPIyX/Rir4rlV4+Xo6CAPD7dC19+wYJSTY9j8H/ZhvOzHWBUP42U/xqp4GC/7MVbFczuMF5fSAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAACTc0kXANjj3Xff1ZEjR0u6jCKdPZsqSapUyb2EK5FcXJx0+XL2dfdTu3ZdDRz4jxtQEQDcGQhGuCP8+eefOnL0dzmVK/nQUZjsS1eCUeK5rBKu5MawHA8AlCYEI9wxnMq5y7Vup5Iuo1BpMZsk6bausTgsxwMApQn3GAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYLpjgtGOHdu0Y8e2ki4DACBek3H3ci7pAuz13XdbJUmBgQ+XcCUAAF6Tcbe6Y2aMAAAAbjaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQCuS2pqimbOnKqZM6cqNva4Zs+errNnU/OtsyyzLJ89e7pNe8uy3Nvmfpx/2xi79iVJsbHH9dxzT+rllyfotdfy15J3u4KOKe/+LP0+//xTOnEixuZnSUpOTs7XvrDar2fsb0Q/hfVXnP6vt5bk5ORCn79byWnatGnTbkRH6emZcnUto7S0zBvRXT47dmyTJAUFtb8p/ZcEN7eyN2287ja7dm3XmZSLcnGvX9KlFOry2WOSdFvXWByXzx5T5Uqud9XvXEH4PSwey3jlfk1euXKF9u+PVkpKsn777bBiYo4rIyNDzZv72azLzLyyTJJWrlyhffv22rT/7bfD2rdvr822uR9b/LXtr3btS5LmzXtNKSkpOn/+XIG15N2uoGPKu7+/+r3S5ocf9lp/7tjxb/rii0+1Z89um/aF1X6tChujG9Vfcfq/3lq++OJT7d27p8Dn70ZycHCQq2uZQtczYwQAuGapqSnavn2r9XF8fJwMw9B3321TbGyMzbrt27daZ4a++26rDMOwtt++fau2b9+aa9vj1jbffbfNZgYj77ZF7Uu6MqsTHx9nU/d33/1VS97tYmOPF3hMufd39myqTb/x8XE2Px88+LM2bdpk076w2q91diR3f9fTT2H9xcbG2N3/9daSmpqiqKgo6+Pcz9+t5lwie70GZ8+m6uzZs5ozZ0ZJl3LDuLg46fLl7JIu445w4kSMcrKcSrqMUiUn65JiY2Puqt+5gvB7WDyW8YqNjVGlSpW0du0qZWdn5WuXk5OjpUvftlmXlZWlNWu+MtcbNu2zsrLk4JB723Brm5ycHK1Z85VCQ5/U2rWr8m1b1L5CQ5/U0qXh+erLXUve7ZYuDS/wmHLvb82ar3TkyK+Ftlm0aKFycnJs2hd03LmPrbhyj8X19FNYf0uXvm13/9dby9q1q5SVVfDzd6sxYwQAuGa7du2QYRj5lmdnZ1lnRf5iaNeuHdq1a0cBwcOwtrVsa2mTnZ2lXbt2WPeXd9ui9iUp32yRJBnGX7Xk3S5/X/mPbdeuHQX2a5GWdtH6h97SvrDaLXUWV+7+rqefwvor7Dm4GbUU9Dxc7/FcqztmxqhSJXdVquSuiRNfLulSbhhPz3uUmHi+pMu4I7zxxiz9+uepki6jVHF0Lqc6taveVb9zBeH3sHgs42WZSWzcuKm2bNmUL0g4OTmrWrVqSkiIz7XOQW3bBkqStm3bkickOMjB4UpgsWx76tQpZWdnycnJ2bpd27aB+ba92r5q1KiZL8Q4OPy13rZ+B9WoUSNPX7Ys9Rw58muh4cjV1U2ZmRnKyrKtv6DaLeuKK/dYXE8/hfVX2HNwM2pp2zYw3/NwvcdzrZgxAgBcs5CQPnJyyv9vbEdHRw0fPtJmnbOzs3r16quQkD5ydHSwae/s7Gxte2Xb561tHB0d1atXX+v+8m5b1L4kafjw5/PVl7uWvNsNH/58gceUe3+9evUtsF+LZ58dJUdHR5v2hdVuqbO4cvd3Pf0U1t/w4SPt7v96awkJ6SNn54Kfv1uNYAQAuGbu7h5q1+6vdy7WqFFTDg4OCgp6WHXq1LVZ165de1Wq5C53dw8FBbWXg4ODtX27du3Vrl37XNvWs7YJCnpYlSq5W/eXd9ui9iVJderUU40aNW3qDgr6q5a829WpU6/AY8q9v0qV3G36rVGjps3PTZv6qlOnTjbtC6vdUue1jH1BY3St8vZXp05du/u/3lrc3T3UuXNn6+Pcz9+tRjACAFyXkJA+ql//ftWvf7+GD39eDRv62MzwWNblngEICemjhg19bNpbluXeNvfj/NuOtGtf0pVZo3LlyqlmzVpq0CB/LXm3K+iY8u7P0m/58uU1YsRIm58lacCAAfnaF1b79Yz9jeinsP6K0//11jJgwIBCn79bycEo6g6zYkhKuqAqVSrctGv1luvZd9P9DtzbYD/LPUaudTuVdCmFSovZJEm3dY3FkRazSfdzjxHyyHuP0d1+flwPzq3iuVXj5ejooCpVKhS+/qZXAAAAcIcgGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGByLukC7BUU1L6kSwAAmHhNxt3qjglGgYEPl3QJAAATr8m4W3EpDQAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADA5FzSBQD2yr6UqrSYTSVdRqGyL6VK0m1dY3FcOZ6qJV0GANxSBCPcEerXr6/Ll7NLuowinT175depUiX3Eq5EcnFxugHjVVW1a9e9IfUAwJ2CYIQ7wrBhw5SYeL6ky7hjeHrew3gBwDXgHiMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwOR8ozpydHSw+T/sw3jZj7EqHsbLfoxV8TBe9mOsiudWjNfV9uFgGIZx06sAAAC4A3ApDQAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBk11eCHDt2TJMmTVJqaqrc3d01Z84c1atXz6ZNWFiYPv30U3l5eUmSWrZsqVdeeUWSlJ6ersmTJ+vgwYNycnLSxIkT1aFDhxt7JLcJe8ZqwoQJOnLkiPXxkSNHFB4erk6dOhU5jnebOXPm6Ouvv1ZcXJzWrl0rb2/vfG2ys7M1c+ZMbd++XQ4ODho+fLgee+yxq667G9kzXuHh4YqIiJCjo6NcXFw0evRotWvXTpI0adIk7dy5Ux4eHpKkrl276tlnn72lx3Cr2DNWvGb9xZ7x4nXripSUFE2YMEGxsbEqU6aM6tatq+nTp6ty5co27Yo6h0rT+WXveL366qvatWuXypQpI1dXV02ZMkW+vr6SpNDQUMXHx6tChQqSpH/84x/q16/fzSvasENoaKixevVqwzAMY/Xq1UZoaGi+NgsXLjRmz55d4PZhYWHGlClTDMMwjGPHjhkPPfSQceHCBXt2fcexZ6xy+/XXXw1/f38jIyPDMIyix/Fus3fvXiM+Pt7o0KGDceTIkQLbrFq1ynjyySeN7OxsIykpyWjXrp1x4sSJq667G9kzXtu2bTPS0tIMw7hybj344INGenq6YRiGMXHiROPjjz++ZfWWJHvGitesv9gzXrmV5tetlBBt4RAAAA6TSURBVJQU4/vvv7c+nj17tjF58uR87Yo6h0rT+WXveH377bdGZmam9edOnTpZ1w0aNMj49ttvb36xpqteSktKStKhQ4fUs2dPSVLPnj116NAhJScn2x2+IiMj1b9/f0lSvXr11KxZM23btu0ao9zt61rG6j//+Y9CQkJUpkyZW1XmbaNVq1aqXr16kW0iIiL02GOPydHRUZUrV1bnzp21YcOGq667G9kzXu3atVP58uUlST4+PjIMQ6mpqbeivNuKPWNVlNLymmVR3PEqza9b7u7uCggIsD5u0aKF4uPj87Ur6hwqTeeXvePVoUMHubi4WNucPHlSOTk5t6zO3K4ajBISElStWjU5OTlJkpycnOTl5aWEhIR8bdevX6+QkBA9+eST2r9/v3V5fHy8atasaX1cvXp1nTx58kbUf1spzlhJUmZmptauXZtvSrCwcSyNEhISVKNGDevj3OdOUesgrV69WnXq1NG9995rXbZ8+XKFhIToueee0x9//FGC1d0eSvtr1rXgdesvOTk5WrFihTp27JhvXVHnUGk9v4oar9w++eQTBQcHy9Hxr4gyd+5chYSEaNy4cTp16tRNrdOue4zsMWDAAD3zzDNycXHRjh079NxzzykiIsJ6PwPyi4qKUo0aNdS4cWPrMsYRN8KePXv01ltv6f3337cuGz16tDw9PeXo6KjVq1fr6aefVlRUlDXIlzb8rl0bXrf+MmPGDLm6umrQoEElXcodwZ7xWr9+vdauXatPPvnEumzu3LmqXr26srOztWTJEr300ktasWLFTavzqjNG1atX16lTp5SdnS3pyg2vp0+fzjft6unpaZ0GCwwMVPXq1XX06FFJUo0aNRQXF2dtm5CQYPOv2LuFvWNl8eWXX+b7V1dR41gaVa9e3WbaNfe5U9S60mz//v0aP368wsPDVb9+fevyatWqWf8F1rt3b6WlpZWKf6UWhtesa8Pr1hVz5sxRTEyM3nzzTZuZDYuizqHSeH5dbbwk6ZtvvtGCBQv03nvvqWrVqtbllr+hTk5O+sc//qGffvrppl5mu2owqlKliho3bqx169ZJktatW6fGjRvnu6M899TWr7/+qri4ON13332Srrz75fPPP5ckHT9+XD///LP1nTJ3E3vHSpJOnjypH374QSEhITbLixrH0qhr165auXKlcnJylJycrKioKHXp0uWq60qrAwcOaPTo0Vq4cKGaNm1qsy73ubV9+3Y5OjqqWrVqt7rE2wavWcXH69YVb7zxhn755ReFh4cXep9VUedQaTu/7BmvzZs36/XXX9d7772nWrVqWZdnZWXpzJkz1sfr16+Xt7d3oeHqRnAwDMO4WqM//vhDkyZN0rlz51SxYkXNmTNH9evX17BhwzRq1Cj5+vpq4sSJOnjwoPVtwqNGjVL79u0lSWlpaZo0aZJ+/fVXOTo6avz48ercufNNO6iSZM9YSdKiRYv022+/acGCBTbbFzWOd5uZM2dq48aNOnPmjDw8POTu7q7169fbjFV2dramT5+uHTt2SJKGDRtmvWmxqHV3I3vGq1+/foqLi7MJPHPnzpWPj4+GDBmipKQkOTg4qEKFCpowYYJatGhRgkd089gzVrxm/cWe8ZJ43ZKko0ePqmfPnqpXr57KlSsnSapVq5bCw8P16KOPaunSpapWrVqR51BpOr/sHa82bdrIxcXFZiLhgw8+UNmyZTVo0CBdvnxZkuTl5aUpU6bYzIbfaHYFIwAAgNKAT74GAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjIA7iI+Pj2JiYkq6DBuhoaFauXLlDenrZh/f008/rVWrVt20/u3VsWNH7dy5s8g2U6ZMUffu3ZWQkKDBgwffosoAEIyAW+ipp57SW2+9lW95VFSUAgMDlZWVVQJVlR7Lli1Tnz59SroMu6SkpGj+/PkaPXq0unXrVtLlAKUGwQi4hfr06aM1a9Yo78eHrVmzRiEhIXJ2vmFfX2iDwHXnsDxX77zzjpo0aaLPPvtMAwYMKOGqgNKDYATcQp07d1Zqaqqio6Oty86ePavNmzerd+/eOnDggPr3769WrVopKChI06dPV2ZmZoF9nT9/XhMmTFCbNm3UoUMHvfPOO9bvD/rqq680YMAAzZo1SwEBAQoLC1NmZqbmzJmj4OBgPfTQQ5o6daouXbokSUpOTtaIESPUqlUr+fv7a+DAgYV+F9GOHTvUtWtXPfjgg5o+fbpNyMvJydE777yjDh06qG3btpowYYLOnz9f6HgsW7ZMQUFBCgoK0n/+8x+bdddS79KlSzVq1CibfmbOnKmZM2dKsr3sV5xad+/erYcffliLFy9WQECAOnbsqDVr1ljXb9myRb1791bLli3Vvn17hYWF2Wy/evVqdejQQQEBAVq0aJHNurCwMI0aNUrjxo1Ty5YttWrVqqueB/v27VO/fv304IMPql+/ftq3b1+hYwygeAhGwC1Urlw5devWTatXr7Yui4yMVP369dWoUSM5Ojpq8uTJ+v777/XZZ59p165d+vTTTwvsa8aMGTp//ryioqL08ccf67///a++/PJL6/oDBw6odu3a2rFjh5599lnNnz9fx44d0+rVq7Vx40adPn1a4eHhkqTly5erWrVq2rVrl3bs2KExY8bIwcEh3z6Tk5M1cuRIvfTSS/r+++9Vp04dmz/KX331lVatWqWPPvpIUVFRSktL0/Tp0wusf9u2bXr//ff1/vvva+PGjdq1a5fN+mupt0ePHtq6dasuXLgg6crXxmzYsEE9e/bMt//i1CpJZ86cUUpKirZv367Zs2dr6tSp+vPPPyVJ5cuX15w5cxQdHa0lS5ZoxYoVioqKkiT9/vvvevXVVzV37lxt375dqamp+b68d9OmTeratauio6MVEhJS5HmQmpqqESNGKDQ0VLt379bQoUM1YsQIpaSkFFo7APsRjIBbrHfv3vr666+VkZEh6cpsguW+l2bNmqlFixZydnZWrVq11L9/f+3duzdfH9nZ2YqIiNDYsWNVoUIF1apVS0OHDrWZxfDy8lJoaKicnZ1VtmxZffHFF/rnP/8pd3d3VahQQSNGjND69eslSc7OzkpMTFR8fLxcXFzUqlWrAoPRtm3b1LBhQ3Xt2lUuLi4aPHiwzbdgr127VkOGDFHt2rXl5uamMWPGKCIiosBLeZGRkerbt6+8vb3l6uqqkSNHWtcZhnFN9dasWVNNmjSxhpLvv/9e5cqVK/A74YpTq8WLL76oMmXKyN/fX+3bt1dkZKQkKSAgQD4+PnJ0dFSjRo3Uo0cP7dmzR5K0YcMGBQcHq3Xr1ipTpoxefPHFfF+A2aJFC3Xu3FmOjo4qV65ckefBli1bVLduXfXu3VvOzs7q2bOn6tevr82bNxdaNwD73ZwbGgAUqlWrVvLw8FBUVJR8fX31888/6+2335YkHTt2TLNnz9Yvv/yi9PR0ZWdnq2nTpvn6SElJ0eXLl1WjRg3rsho1ath8y/m9995r/Tk5OVnp6enq27evdZlhGNbLZU899ZTefvttPfnkk5Kk/v37a/jw4fn2e/r0aZt+HRwcVL16dZv1NWvWtD6uWbOmsrKylJSUZPPFtpa2zZo1s2l7I+rt2bOn1q1bp969e2vdunUFzhYVt1ZJqlixolxdXa2Pa9SoodOnT0uSfvrpJ82fP19Hjx7V5cuXlZmZqa5duxY4Zq6urnJ3d7fpO/d6qejz4PTp0zbPu6WW3M89gGvHjBFQAh599FGtXr1aa9asUVBQkHXWZdq0aapfv76+/vpr7du3T6NHj853o7YkeXh4yMXFRfHx8dZlCQkJNn/Qc8/4eHh4qFy5clq/fr2io6MVHR2tH374Qfv375ckVahQQZMmTdKmTZu0aNEiLV++PN+lLUny9PS0uQxkGIYSEhKsj728vBQXF2d9HB8fL2dnZ1WpUiVfX15eXjbb5j6W66m3W7du2rNnj06ePKlvvvlGISEh+fZd3Fol6dy5c0pLS7M+TkhIkJeXlyRp7Nix6tSpk7Zu3aoffvhBAwYMsD5vXl5eNmOWnp6u1NRUm77zzs4VdR54eXnZjJWlloLCHIDiIxgBJaB3797atWuXvvjiC/Xu3du6/OLFi3Jzc5Obm5v++OMPrVixosDtnZyc1LVrVy1YsEAXLlxQXFycli9frl69ehXY3tHRUY899phmzZqlpKQkSdKpU6e0fft2SdLmzZsVExMjwzB0zz33yMnJqcBLae3bt9fRo0e1ceNGZWVl6aOPPtKZM2es63v27KkPP/xQJ06c0MWLF7VgwQJ169atwHfbde3aVatWrdLvv/+u9PR066zZ9dZbuXJl+fv7a/LkyapVq5YaNGhQ4JgUp1YLy03s0dHR2rJli3VW6OLFi6pUqZLKli2rAwcOaN26ddZtunTpoi1btig6OlqZmZlauHBhoTe2WxR1HrRv317Hjx/X2rVrlZWVpYiICP3+++8KDg4usk8A9iEYASWgVq1a8vPzU3p6ujp16mRdPnHiRK1bt04tW7bUyy+/rO7duxfax8svv6zy5curc+fOGjhwoHr27Kl+/foV2n78+PGqW7euHn/8cbVs2VJDhgzRsWPHJEkxMTEaOnSo/Pz81L9/fz3xxBNq06ZNvj4qV66st956S//6178UEBCgmJgYtWzZ0rq+X79+6tWrlwYNGqROnTqpTJkyevnllwusp3379ho8eLAGDx6sRx55JN/+rqfenj17aufOnYVeRiturZJUtWpVVaxYUe3atdO4ceM0bdo0a+h65ZVXtHDhQvn5+Sk8PNzmc4caNmyoqVOnaty4cWrXrp0qVqyY79JZXkWdBx4eHlq8eLGWL1+ugIAALVu2TIsXL1blypWL7BOAfRyMgubpAQBWu3fv1vjx47Vt27aSLgXATcaMEQAAgIlgBAAAYOJSGgAAgIkZIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADA9P9ucIVGblB3BAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjaucMJQmjIt"
      },
      "source": [
        "- Com este boxplot podemos concluir que 3/4 dos genes têm um desvio padrão entre 1.00 e 1.28. E que a muitos outliers acima de 1.75\n",
        "\n",
        "Referências: [Como interpretar um boxplot](https://operdata.com.br/blog/como-interpretar-um-boxplot/); [Wikipedia](https://pt.wikipedia.org/wiki/Diagrama_de_caixa)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hj_fO8VIzSO"
      },
      "source": [
        "#####Ordenando os dados estatístico dos genes g_*  pelo desvio padrão de forma decrescente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "5rs3urpNHkIX",
        "outputId": "923a991b-1bde-4e35-b033-85a4cd9ca51a"
      },
      "source": [
        "transposta_g.sort_values('std',ascending=False)"
      ],
      "execution_count": 20,
      "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_50</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.588018</td>\n",
              "      <td>2.256983</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.563700</td>\n",
              "      <td>0.01010</td>\n",
              "      <td>0.496875</td>\n",
              "      <td>4.889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_75</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.445392</td>\n",
              "      <td>2.254701</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.546850</td>\n",
              "      <td>0.01000</td>\n",
              "      <td>0.529975</td>\n",
              "      <td>8.835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_37</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.606710</td>\n",
              "      <td>2.200277</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.604100</td>\n",
              "      <td>-0.00070</td>\n",
              "      <td>0.460500</td>\n",
              "      <td>5.602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_228</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.475231</td>\n",
              "      <td>2.163439</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.545400</td>\n",
              "      <td>0.01385</td>\n",
              "      <td>0.488375</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_100</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.553186</td>\n",
              "      <td>2.152067</td>\n",
              "      <td>-5.915</td>\n",
              "      <td>-0.519275</td>\n",
              "      <td>-0.06260</td>\n",
              "      <td>0.544700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_331</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.072841</td>\n",
              "      <td>0.630456</td>\n",
              "      <td>-2.996</td>\n",
              "      <td>-0.253000</td>\n",
              "      <td>0.00140</td>\n",
              "      <td>0.299100</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_550</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.033976</td>\n",
              "      <td>0.623064</td>\n",
              "      <td>-5.238</td>\n",
              "      <td>-0.371700</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.349675</td>\n",
              "      <td>3.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_104</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.079709</td>\n",
              "      <td>0.621391</td>\n",
              "      <td>-3.138</td>\n",
              "      <td>-0.208800</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.290300</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_307</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.131650</td>\n",
              "      <td>0.602029</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.265300</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>5.584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_219</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.046375</td>\n",
              "      <td>0.577300</td>\n",
              "      <td>-2.891</td>\n",
              "      <td>-0.259700</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.300800</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>772 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean       std     min       25%      50%       75%     max\n",
              "g_50   23814.0 -0.588018  2.256983 -10.000 -0.563700  0.01010  0.496875   4.889\n",
              "g_75   23814.0 -0.445392  2.254701 -10.000 -0.546850  0.01000  0.529975   8.835\n",
              "g_37   23814.0 -0.606710  2.200277 -10.000 -0.604100 -0.00070  0.460500   5.602\n",
              "g_228  23814.0 -0.475231  2.163439 -10.000 -0.545400  0.01385  0.488375  10.000\n",
              "g_100  23814.0  0.553186  2.152067  -5.915 -0.519275 -0.06260  0.544700  10.000\n",
              "...        ...       ...       ...     ...       ...      ...       ...     ...\n",
              "g_331  23814.0  0.072841  0.630456  -2.996 -0.253000  0.00140  0.299100  10.000\n",
              "g_550  23814.0 -0.033976  0.623064  -5.238 -0.371700  0.00000  0.349675   3.669\n",
              "g_104  23814.0  0.079709  0.621391  -3.138 -0.208800  0.00000  0.290300  10.000\n",
              "g_307  23814.0 -0.131650  0.602029 -10.000 -0.265300  0.00000  0.000300   5.584\n",
              "g_219  23814.0  0.046375  0.577300  -2.891 -0.259700  0.00000  0.300800  10.000\n",
              "\n",
              "[772 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKcBqfeHFEWU"
      },
      "source": [
        "Agora vamos comparar o comportamento dos genes com maior e menor desvio padrão."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "pZ-LuSj6EtQ7",
        "outputId": "7f597d87-2eb7-4eac-d34b-dd45adf05a97"
      },
      "source": [
        "# Colocando quatro gráficos para comparação\n",
        "fig, ax3 = plt.subplots(2, 2,figsize=(18, 12))\n",
        "plt.suptitle('Extremos do desvio padrão', size=18, y=0.93)\n",
        "\n",
        "# Maiores desvios padrão g_50, g_75\n",
        "hist1 = sns.histplot(data=dados, x='g_50', bins=100, ax=ax3[0,0], color='blue')\n",
        "hist1.set_xlabel ('Maior Desvio padrão g_50', size=12)\n",
        "hist1.set_ylabel('Quantidade', size=12)\n",
        "\n",
        "hist2 = sns.histplot(data=dados, x='g_75', bins=100, ax=ax3[0,1], color='green')\n",
        "hist2.set_xlabel ('Segundo maior desvio padrão g_75', size=12)\n",
        "hist2.set_ylabel('')\n",
        "\n",
        "# Menores desvios padrão g_307 e g_219\n",
        "hist3 = sns.histplot(data=dados, x='g_219', bins=100, ax=ax3[1,0], color='red')\n",
        "hist3.set_xlabel ('Menor desvio padrão g_219', size=12)\n",
        "hist3.set_ylabel('Quantidade', size=12)\n",
        "\n",
        "hist4 = sns.histplot(data=dados, x='g_307', bins=100, ax=ax3[1,1], color='brown')\n",
        "hist4.set_xlabel ('Segundo menor desvio padrão g_307', size=12)\n",
        "hist4.set_ylabel('')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ozT9SvBqmeR"
      },
      "source": [
        "Um desvio padrão grande significa que os valores amostrais estão bem distribuídos em torno da média, como vemos nos dois primeiros gráficos.\n",
        "Enquanto que um desvio padrão pequeno indica que eles estão condensados próximos da média, ou seja a amostra é mais homogênea, como vemos nos dois últimos gráficos. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pH2ZWEqLdTIk"
      },
      "source": [
        "####Desafio 07\n",
        "Resumo do que vc aprendeu com os dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQXEXizIl21e"
      },
      "source": [
        "Nesta aula vimos como utilizar a biblioteca Seaborn e como melhorar a apresentação dos gráficos, que facilitam a visualização e interpretação dos dados. \n",
        "- Tive a oportunidade de aprender como incrementar alguns gráficos, como histograma e boxplot, além de criar comparativos mostrando mais de um gráfico ao mesmo tempo, o que eu nunca havia feito. \n",
        "- Com o entendimento do conceito de desvio padrão foi possível perceber como algumas amostras são mais homogêneas do que outras. \n",
        "- É necessário ainda analisar os compostos mais utilizados e como se comportam nos diferentes tempos. "
      ]
    }
  ]
}