Notebook_Imersão_Dados_3ª_ED.ipynb

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  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Notebook Imersão Dados 3ª ED.ipynb",
      "provenance": [],
      "mount_file_id": "1Er3avWOZJAOFykbjR5NRiz-L8uiPSPpF",
      "authorship_tag": "ABX9TyNdSc/rDDwhKvK4hrah0qZq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ConradBitt/imersao_dados_3ed/blob/main/Notebook_Imers%C3%A3o_Dados_3%C2%AA_ED.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Px021m2F8B0U"
      },
      "source": [
        "# Python Pandas e para um problema de farmácologia\n",
        "\n",
        "## Resumo\n",
        "Neste tópico iremos utilizar o python junto com o módulo [Pandas](pandas.pydata.org/), [Matplotlib](matplotlib.org/) e [Seaborn](seaborn.pydata.org/), para analisar alguns dados referêntes a experimentos em biologia molecular. Explorando quais os tipos das variaveis, o que é tratamento com droga e controle, quais substancias mais ministradas e por fim renomeando algumas variaveis para facilitar a manipulação dos dados.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjqnh5Zy9UIK"
      },
      "source": [
        "## Importações"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJZzimREA5X3"
      },
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib as mpl\n",
        "\n",
        "sns.set_context('talk')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFQOulMxA6Tv"
      },
      "source": [
        "## Obtendo dados "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COny6h9fBpi4"
      },
      "source": [
        "uri_experimentos = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "uri_resultados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLCuD1g9A_IJ"
      },
      "source": [
        "experimentos = pd.read_csv(uri_experimentos,\n",
        "                           compression='zip')\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xlPm2pE8EL22"
      },
      "source": [
        "## Exploração \n",
        "\n",
        "Aqui temos os dados referentes à um experimento em genética"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "mjDR-tmfBOz9",
        "outputId": "71cc5ae4-9345-4880-fca6-fa15b98529c6"
      },
      "source": [
        "display(experimentos.tail())\n",
        "display(experimentos.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      <td>24</td>\n",
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              "      <td>id_fffc1c3f4</td>\n",
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              "      <td>48</td>\n",
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              "      <td>0.2807</td>\n",
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              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
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              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
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              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
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              "      <td>0.2024</td>\n",
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              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
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              "      <td>-0.3372</td>\n",
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              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
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              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.9170</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
              "23809  id_fffb1ceed     com_droga     24   D2  ...  0.0631  0.9171  0.5258  0.4680\n",
              "23810  id_fffb70c0c     com_droga     24   D2  ... -0.2084 -0.1224 -0.2715  0.3689\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "23813  id_ffffdd77b     com_droga     72   D1  ... -3.5770 -0.4775 -2.1500 -4.2520\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(23814, 877)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZP8kJu_fCCtN"
      },
      "source": [
        "Aparentemente são dados experimentais de teste de algumas drogas/farmacos/compostos, nas colunas temos as variáveis e linhas amostras.\n",
        "\n",
        "Temos um total de 23814 amostras, cada uma com 877 variáveis. Podemos analisar que tipo de dado apresenta cada variável com o método ``info()``"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0gB5_TkpZQ_"
      },
      "source": [
        "### Tipos de variáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lvOa610DlpI",
        "outputId": "6ca18432-f436-45fd-f4c1-cb22f1dc6067"
      },
      "source": [
        "experimentos.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23814 entries, 0 to 23813\n",
            "Columns: 877 entries, id to c-99\n",
            "dtypes: float64(872), int64(1), object(4)\n",
            "memory usage: 159.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm43wy-lDurg"
      },
      "source": [
        "\n",
        "#### Cometario sobre os tipos de variáveis\n",
        "> Temos dados do tipo float64 (872 variávei), int64(uma variável), object (quatro variáveis).\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fg99aZCbppQE"
      },
      "source": [
        "### Sobre categorias de tratamento\n",
        "\n",
        "É possivel notar a presença de uma variável chamada ``tratamento`` indica se aquela amostra pertence ao grupo ``com_controle`` ou ao grupo ``com_droga``. Em pesquisas é extremamente comum ter estes dois grupos de amostras quando se quer testar exclusivamente determinados compostos, dado que as amostras estão submetidas à mesmas condições, se ministra um tratamento num grupo de controle e em outro grupo com a droga, assim é possível comparar se alterações ocorreram apenas sob influencia do fármaco ou não.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "HgFWcYccFFa8",
        "outputId": "23bcb13b-e5b8-4a9a-97d3-0a6244ce0ff0"
      },
      "source": [
        "tratamentos = experimentos.tratamento.value_counts()\n",
        "\n",
        "ax = sns.barplot(y=tratamentos.index, x=tratamentos.values)\n",
        "ax.figure.set_size_inches(7,5)\n",
        "\n",
        "ax.set_title('Proporção entre categorias', pad=20)\n",
        "ax.set_yticklabels([categoria.replace('_', ' ') for categoria in tratamentos.index])\n",
        "\n",
        "plt.minorticks_on()\n",
        "plt.show()\n",
        "\n",
        "print('Tabela em (%) de amostras em cada tratamento: \\n')\n",
        "tratamentos = (experimentos.tratamento.value_counts(normalize=True) * 100).to_frame()\n",
        "tratamentos.columns = ['Amostras (%)']\n",
        "tratamentos = tratamentos.rename_axis('tratamento')\n",
        "display(tratamentos)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Tabela em (%) de amostras em cada tratamento: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>Amostras (%)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tratamento</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>92.164273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_controle</th>\n",
              "      <td>7.835727</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Amostras (%)\n",
              "tratamento                \n",
              "com_droga        92.164273\n",
              "com_controle      7.835727"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2m18ghLFKHo"
      },
      "source": [
        "#### Comentário sobre categorias de tratamento \n",
        "> Acima vemos quantas amostras pertencem a cada grupo. É possível perceber que a proporção entre os dados não é a mesma, mais de 92% as amostras pertence ao grupo de amostras ``com_droga`` e apenas 7% pertence ao grupo ``com_controle``. **Uma hipótese que é razoável propor é que neste conjunto de experimentos se testava varios compostos ao mesmo tempo dado um mesmo grupo de controle.**\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u20OCdLdnF9y"
      },
      "source": [
        "### Analisando a variável tempo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "2LcBugVWnJBc",
        "outputId": "5a0b11d2-632c-4c13-b0b1-2f8ac3ee926a"
      },
      "source": [
        "tempos_das_amostras = experimentos.tempo.value_counts()\n",
        "\n",
        "ax = sns.barplot(x=tempos_das_amostras.index, y=tempos_das_amostras.values)\n",
        "ax.set_title('Proporção entre categorias', pad=20)\n",
        "ax.figure.set_size_inches(7,5)\n",
        "ax.set_xticklabels([str(tempo) + 'h' for tempo in tempos_das_amostras.index])\n",
        "plt.show()\n",
        "\n",
        "print('Tabela em (%) das amostras no tempo referente: \\n')\n",
        "tempos_das_amostras = (experimentos.tempo.value_counts(normalize=True) * 100).to_frame()\n",
        "tempos_das_amostras.columns = ['Amostras (%)']\n",
        "tempos_das_amostras = tempos_das_amostras.rename_axis('Tempo')\n",
        "tempos_das_amostras"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ySpJrk1yR5NAk649ob2aSQ5JcnuS6JKcnefIEzz1Wm5IkTZXmOcsk9wHOBBYBnwSuBh4HvB/4e2Cvrt62wI+A84HXAvcFXg9sATxzqNmjgOcBHwd+A8wFTkjyhFLKT3rPPZk2JUmaEuMM8NkTmA3sVEo5vys7PMl6wO5J/qWUciPwPuAqYOdSyhKAJJcCRyR5UinlpK5sB2B34IBSyse7snnAecAhwON7zz1Wm5IkTaVxDsNu1E3/OFR+BXAjcHOSjYCnAvMGodaZBywBduuVPb9b7shBQSllKfA5YKckcwAm2aYkSVNmnLD8n276uSQPT3K/JHtQD50eUkq5BdiGupd6Vn/BUsoNwHxgu17xdsCvhgIQ6qHeANt2v0+mzdtJsnB5D2DWGOstSRIwRliWUn4AvJW6lzcfuAz4MjUo39lVm9NNF4xoYgGwae/3OcupR6/uZNqUJGnKjHtTgt8CJwPHUc8hPgN4Z5I/l1I+A6zX1bt+xLJLe/Ppfp6oHr26k2nzdkopsyeaB3XPE/cuJUljGmc07O7AZ4EHl1Iu74q/mWQt4MNJjgau68pnjmhi3d58up8nqkev7mTalCRpyoxzzvLlwNm9oBz4NrAB8HBuO1Q6h2XNAfrLLlhOPXp1J9OmJElTZpyw3AS4y4jydbrp2tTLPm4CHtmvkGQGdcDO/F7xfGDLJBsOtffobnpuN51Mm5IkTZlxwvIi4JFJHjBU/s/AzcDPSymLgBOBvYZCcC9gQ+CYXtmx1KDdb1DQ3QloH+DUwR7sJNuUJGnKjDPA50PA04FTkwzu4PNPXdlnSil/6uodDJwGnJzkSOrddl4HnFBKOXHQWCnljCTHAB/srqm8GHgxsBn1cpS+sdqUJGkqNcOylPLjJI8F3gG8Arg7dXTsm6hBOqh3TpKnUO/C8zFgMXBEV2/Y3sC7u+nGwM+BXUoppw4992TanHLbHzhvOp5WYzr7Q3tPdxe0BtnxEztOdxe0HKf+26ntSpMw1qUjpZQzgV3GqHcK0NyCujv2HNg97pA2JUmaKv6LLkmSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGgxLSZIaDEtJkhoMS0mSGsYOyySPSvK9JH9JsiTJuUnmDtV5VpJzkixNclmStydZe0Rbs5McnuTPSa5JclKSbSd43rHalCRpqowVOkmeDnwLOBl4K3Aj8GDgfkN1jgdOAv4N2AZ4G3CP7vdBvbWA73XzPwxcBbwcODnJ9qWUiyfbpiRJU6kZlklmAUcBny6lvHo5VT8M/Ax4Winl5m7ZxcCbkvx7KeXXXb3nA48Fdi2lHN/V+zpwEfB2YO8VaFOSpCkzzmHYFwGzqXt0JLlrkvQrJHko8FDgs4NQ63yqe47n9cqeD1xO3VMFoJTyZ+DrwHOSrLMCbUqSNGXGCcunAL8Cdknyf8Bi4OokH0hyl67Odt30rP6CpZTLgd/35g/qnl1KKUPPcyZwV+CBK9Dm7SRZuLwHMKu92pIkVeOE5QOp5yaP6h7PA44DDgI+0tWZ000XjFh+AbBp7/c5y6lHr+5k2pQkacqMM8BnQ2Bj4I2llEO6sm8m2RB4eZL3AOt15dePWH4psH7v9/WWU28wvz8dp83bKaXMnmge1D1P3LuUJI1pnD3L67rpV4fKvwKsA+zQqzNzxPLr9uYP2puoXv/5JtOmJElTZpywHBwG/eNQ+eD3jXt15rCsOdQBPf32JqpHr+5k2pQkacqME5Znd9P7DJXft5v+GZjf/fzIfoUkm3b15veK5wPbD4+oBR4NLAF+06s3bpuSJE2ZccLymG6676CgC7r9gGuA00sp51NHzP5rb4QswMuAW4Bv9MqOpQ7OeXavvXsALwC+VUq5EWCSbUqSNGWaA3xKKWcnmUe9EcC9gHOAZwBPA95QSlncVT0Q+Dbw/SRHA1sDr6ReJ3lRr8ljgdOBeUk+DFxJvYPPWsA7hp5+3DYlSZoy494bdn/gvdSAPJR6OclLSykfGlQopXwXeC5wd+AT3c/vAV7Vb6i7wcAu1JsQvAr4EPVQ7hNLKb8ZqjtWm5IkTaWx7g1bSrmBek/YtzbqHU+9l2urvb9QD+PuN0bdsdqUJGmq+C+6JElqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJalihsEzyhiQlyfwR8x6b5JQk1ya5IsmhSdYfUW9mkkOSXJ7kuiSnJ3nyBM83VpuSJE2FSYdlknsDbwGuGTFvW+BHwLrAa4EjgZcAR49o6ijgAODLwKuBW4ATkjzmb2hTkqQ73NorsMwHgLOoQTt7aN77gKuAnUspSwCSXAockeRJpZSTurIdgN2BA0opH+/K5gHnAYcAj59sm5IkTZVJ7Vl2IbcndQ9veN5GwFOBeYNQ68wDlgC79cqeD9xI3UsEoJSyFPgcsFOSOSvQpiRJU2LssEwS4BPAF0spy5yrBLah7qme1S8spdwAzAe26xVvB/xqKAABzgQCbLsCbUqSNCUmcxh2b+ChwHMmmD+nmy4YMW8B8Jihun+YoB7ApivQ5q2SLJygjwOzGvMlSbrVWHuWSe5KPVf5gVLKqOACWK+bXj9i3tLe/EHdier125pMm5IkTYlx9yzfAtwAfHQ5da7rpjNHzFu3N39Qd6J6/bYm0+atSinDA49up9vzdO9SkjSWZlh2g21eA7wV2KSeugRqWM1Icn9gEbcdKp3DsuYAl/d+X7CcevTqTqZNSZKmxDiHYTcBZlAv6fht7/FoYKvu54Ool33cBDyyv3CSGdQBO/1BQfOBLZNsOPRcj+6m53bTybQpSdKUGCcsfwvsOuJxPnBp9/O8Usoi4ERgr6EQ3AvYEDimV3YssA6w36AgyUxgH+DUUsrlAJNsU5KkKdE8DNsF1vHD5UleA9xUSunPOxg4DTg5yZHAfYHXASeUUk7stXlGkmOAD3aHeS8GXgxsBswdeqqx2pQkaarcoTdSL6WcAzyFOnr1Y8D+wBHAC0ZU3xs4tJv+O3VPc5dSyql/Q5uSJN3hVuR2dwCUUnaeoPwUYMcxll8KHNg9WnXHalOSpKngv+iSJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKnBsJQkqcGwlCSpwbCUJKmhGZZJHpXksCS/THJNksuSfC3JA0fUfWySU5Jcm+SKJIcmWX9EvZlJDklyeZLrkpye5MkTPP9YbUqSNFXG2bM8CHgucCLwauBwYGfgZ0m2GlRKsi3wI2Bd4LXAkcBLgKNHtHkUcADw5a7NW4ATkjymX2mSbUqSNCXWHqPOR4EXlVJuGBQkORr4BTVI53bF7wOuAnYupSzp6l0KHJHkSaWUk7qyHYDdgQNKKR/vyuYB5wGHAI/vPfdYbUqSNJWae5allNP6QdmV/Ro4H9gKIMlGwFOBeYNQ68wDlgC79cqeD9xI3UsctLcU+BywU5I5K9CmJElTZoUG+CQJsAlwZVe0DXUv9ax+vS5k5wPb9Yq3A341FIAAZwIBtl2BNiVJmjLjHIYdZQ/gPsDB3e9zuumCEXUXAP1zkXOAP0xQD2DTFWjzdpIsnGheZ1ZjviRJt5r0nmWSLYHDgFOAL3XF63XT60cssrQ3f1B3onr9tibTpiRJU2ZSe5ZJ7g18D/gL8IJSyi3drOu66cwRi63bmz+oO1G9fluTafN2SimzJ5oHt+55uncpSRrL2GGZZBZwAjVkdiylXNGbPThUOmeZBWvZ5UN1J6pHr+5k2pQkacqMdRg2ybrAd4AHA/9USrlwqMp5wE3AI4eWm0EdsDO/Vzwf2DLJhkNtPLqbnrsCbUqSNGXGuYPPXag3AXgM9dDr6cN1SimLqDct2GsoBPcCNgSO6ZUdC6wD7Nd7jpnAPsCppZTLV6BNSZKmzDiHYT8CPIu6Z3m3JHv25i0ppRzf/XwwcBpwcpIjgfsCrwNOKKWcOFiglHJGkmOAD3bXVF4MvBjYjNtucMBk2pQkaSqNE5aD6x6f2T36fgccD1BKOSfJU6h34fkYsBg4AnjTiDb3Bt7dTTcGfg7sUko5tV9pkm1KkjQlmmFZStl53MZKKacAO45RbylwYPe4Q9qUJGmq+C+6JElqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqMCwlSWowLCVJajAsJUlqWCXCMsnMJIckuTzJdUlOT/Lk6e6XJGnNsEqEJXAUcADwZeDVwC3ACUkeM52dkiStGdae7g60JNkB2B04oJTy8a5sHnAecAjw+GnsniRpDbAq7Fk+H7gROHJQUEpZCnwO2CnJnOnqmCRpzZBSynT3YbmS/BDYpJTysKHyJwMnAruUUk4Ymrew0ewsgFmzZk2qL0uW3jip+lq5Nlx3nZXyPOX6v66U59GKycy7rpTnWXLDkpXyPFoxG87YcFL1Fy1aBFBKKSN3Iu/0h2GBOcAfRpQv6KabrmC7ZdGiRYtXcNnVweCbwqJp7cUdaNH1092DVdJqtx2wdPVZlZVotdsOFl036VXZiDoeZqRVISzXA0Z9DC7tzb+dUsrsKe3RamCw9+1rtWZzOxC4HYxjVThneR0wc0T5ur35kiRNmVUhLBdQD8UOG5RdvhL7IklaA60KYTkf2DLJ8NnaR3fTc1dyfyRJa5hVISyPBdYB9hsUJJkJ7AOcWkpxz1KSNKXu9AN8SilnJDkG+GB3TeXFwIuBzYC509k3SdKa4U4flp29gXd3042Bn1Ovrzx1WnslSVoj3OlvSqCp4VBxgduBKreDNsNSkqSGVWGAjyRJ08qwlCSpwbCUJKnBsNQykrwjSUniyf41mNuBwO1gwLBcDSV5Q7dxzx8qXyvJS5Ocm2RJkgVJvp3kkdPVV91xkhzVve8TPe6TZP0kr0jyw+79/2uSc7rt4i7TvQ762yV5VJLDkvwyyTVJLkvytSQPXM4yGyW5ottOnrMy+7uqWFWus9SYktwbeAtwzYjZhwCvB74MHAbcDXgpcEqS7Usp56+0jmoqfJb6P177AnwGuLSU8ockWwOfAH4EfBRYDDwN+DTwKGDfldddTZGDgB2BY6jXpN8beCXwsyQ7lFIuGBv3P/oAAARcSURBVLHMW4HJ/QPINYxhufr5AHAW9ajBrYdNkqwFvAw4tpSyV6/8u8AvgN2At6/cruqOVEr5CfCTflmSnYD1ga90RVcA2wx9Mfpsks8D/5LkPaWU366UDmuqfBR4USnlhkFBkqOpf+cHMXTnsyQPBl4FvA94x0rr5SrGw7CrkSQ7AHsCrx0xe23qh+Yfh8qv6Kaj/tXZxknmJVnUPb6QZP07rsdaCV4EFOA/AEopV05wBOG4brrliHluB6uQUspp/aDsyn4NnA9sNWKRjwHfBf6n0fQavR0YlquJJKEeXvtiKWX+8Pzuj+d0YG6SPZLcL8nDgc9R/w3aF0c0+w3qP9d+I/B16jdS9z5XEUnWoR4xOK2Ucmmj+r276ZUj5rkdrOK6z4dNGHp/k+wCPAV4wxjNrNHbgYdhVx97Aw8Flndyfm/gaOo5y4GLgJ1KKQtG1P9pKeUlg1+S3J16Tuugv727WgmeBtyd2w7BjpRkBvAa6j8pOHtEFbeDVd8ewH2AgwcF3ZepjwGfKKVcnOR+jTbW6O3APcvVQJK7Us9VfmCC0BtYDJxH3QN9LvByYF3gO0nuNqL+Z4Z+/1/g7kk2+tt7rZXgRcCN1L2A5fkk9fDcK0opt4yY73awCkuyJXVA3ynAl3qzXk0d5PeeMZtao7cDw3L18BbgBuqJ/ZGSrE0dAXl1KeVVpZTjSimfph6CeRDwuhGLXTb0+1+66cZ/e5c1lbp/lv5s4PullKuWU+9AYH/gzaWU709Qze1gFdWNjv8e9T17weDLUJJNqCNg315KWThmc2v0duBh2FVc9z8+X0Pd8DeppyaAusc4I8n9gUXAdsDW1FFvtyql/DrJBdSh5sNunuhp/+aOa6o9h9uPgl1GkrnUy4kOK6V8YDltuR2sgpLMAk4AZgE7llKu6M0+mPq58IPuMwJuO299r67sd+X2/2ljjd4ODMtV3ybADOqH3iEj5v+2Kz+3+33Uhefr4LawutkDWAJ8e9TMJM8GjqQO2njVqDpadSVZF/gO8GDgyaWUC4eq/B1wP+DXIxb/bDddD1g6ZZ1cxfgBuer7LbDriPL3ABsAB1AH8azXle9O78L1JI8AHgJ8amq7qZUlyT2ph9e/Wkq5dsT8xwNfA34M7DnBeUqtoro7MR0NPAZ4dinl9BHV3g8cNVS2NfBu6viHM6indtQxLFdxpZRFwPHD5UleA9xUSjm+V/ZDYN/uHo8nAnOAf6Pe7efQldNjrQQvpP5tL3MINslm1L3NAhwLvKB36B7qZSaXrIxOasp8BHgWdc/ybkn27M1bUko5vpRyxvBCg38ADZzR/9xQZViuWZ5Nvd3d7sAuwPXUEW1vKaX8Zjo7pjvUHsCfWPbWdwCbU89hQR0hOWwfwLBctW3bTZ/ZPfp+x4gv12rL7c/fSpKkYV46IklSg2EpSVKDYSlJUoNhKUlSg2EpSVKDYSlJUoNhKUlSg2EpSVKDYSlJUoNhKUlSw/8HP67diSwMB1UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Tabela em (%) das amostras no tempo referente: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Amostras (%)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tempo</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>34.643487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>32.720249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>32.636264</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Amostras (%)\n",
              "Tempo              \n",
              "48        34.643487\n",
              "72        32.720249\n",
              "24        32.636264"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vh1PmuvhmbX_"
      },
      "source": [
        "#### Comentário sobre a categoria tempo\n",
        "> Podemos verificar que as categorias são $48$, $72$ e $24$, possivelmente expresso em horas. A proporção dos dados nas categorias é bem homogênea.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbxWPwHe6-SC"
      },
      "source": [
        "### Drogas ministradas\n",
        "\n",
        "Dentro dos experimentos temos uma variável chamada ``droga``, nela estão os tipos de drogas ministradas em cada amostra. Esta variável esta anonimizada justamente para que o composto aplicado não seja enviesado por quem pesquisa e analisa os dados. Mas podemos investigar quais tipos de drogas foram ministradas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "ceM3Ry2O7AJy",
        "outputId": "1d6ee17d-f1e6-4dfb-c637-f1c0b1f81cae"
      },
      "source": [
        "drogas_ministradas = experimentos.query('tratamento == \"com_droga\"')['droga'].value_counts()\n",
        "display(drogas_ministradas.to_frame())\n",
        "\n",
        "\n",
        "dez_drogas_mais_ministrada = drogas_ministradas[:10]\n",
        "\n",
        "ax = sns.barplot(x=dez_drogas_mais_ministrada.values,\n",
        "                 y=dez_drogas_mais_ministrada.index, palette='tab20')\n",
        "ax.figure.set_size_inches(8,5.5)\n",
        "ax.set_title('Dez drogas mais ministradas', pad=20)\n",
        "ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())\n",
        "plt.show()\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "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>droga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87d714366</th>\n",
              "      <td>718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9f80f3f77</th>\n",
              "      <td>246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8b87a7a83</th>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5628cb3ee</th>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d08af5d4b</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a67f38a69</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b2fe3eca7</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98c4c858e</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4055d26a8</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35519ef6d</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3288 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           droga\n",
              "87d714366    718\n",
              "9f80f3f77    246\n",
              "8b87a7a83    203\n",
              "5628cb3ee    202\n",
              "d08af5d4b    196\n",
              "...          ...\n",
              "a67f38a69      1\n",
              "b2fe3eca7      1\n",
              "98c4c858e      1\n",
              "4055d26a8      1\n",
              "35519ef6d      1\n",
              "\n",
              "[3288 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWmIZsNk8TqO"
      },
      "source": [
        "#### Comentario sobre quantos tipos de drogas foram ministradas\n",
        "\n",
        "> Dentre as amostras classificadas como \"com_droga\" foram ministradas 3288 substancias diferentes no experimento. É possível notar que muitas drogas foram aplicadas em apenas uma amostra. Dado que algumas drogas tem uma baixa amostragem vou exibir as 10 substancias mais frequentes.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i6IPORZAFFkC"
      },
      "source": [
        "\n",
        "\n",
        "### Medidas de tendencia central, dispersão e intervalo das variáveis\n",
        "Outro ponto importante de se dar uma breve olhada é nas medidas de tendência central, dispersão e intervalos interquartis, podemos fazer isso através do método `describe()`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OTAl2CG7DQfR",
        "outputId": "08f5f965-6eb4-4d5c-c11d-16958fbfb69e"
      },
      "source": [
        "experimentos.describe().T"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>tempo</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>48.020156</td>\n",
              "      <td>19.402807</td>\n",
              "      <td>24.000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>48.00000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>72.000</td>\n",
              "    </tr>\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>...</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>c-95</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.353726</td>\n",
              "      <td>1.629291</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.567975</td>\n",
              "      <td>-0.01025</td>\n",
              "      <td>0.444750</td>\n",
              "      <td>2.814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-96</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.463485</td>\n",
              "      <td>2.059725</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.552575</td>\n",
              "      <td>-0.00125</td>\n",
              "      <td>0.465225</td>\n",
              "      <td>3.505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-97</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.378241</td>\n",
              "      <td>1.703615</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.561000</td>\n",
              "      <td>-0.00680</td>\n",
              "      <td>0.446400</td>\n",
              "      <td>2.924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-98</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.470252</td>\n",
              "      <td>1.834828</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.592600</td>\n",
              "      <td>0.01400</td>\n",
              "      <td>0.461275</td>\n",
              "      <td>3.111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-99</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.301505</td>\n",
              "      <td>1.407918</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.562900</td>\n",
              "      <td>-0.01950</td>\n",
              "      <td>0.438650</td>\n",
              "      <td>3.805</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>873 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count       mean        std  ...       50%        75%     max\n",
              "tempo  23814.0  48.020156  19.402807  ...  48.00000  72.000000  72.000\n",
              "g-0    23814.0   0.248366   1.393399  ...  -0.00885   0.525700  10.000\n",
              "g-1    23814.0  -0.095684   0.812363  ...  -0.04660   0.403075   5.039\n",
              "g-2    23814.0   0.152253   1.035731  ...   0.07520   0.663925   8.257\n",
              "g-3    23814.0   0.081971   0.950012  ...   0.00805   0.463400  10.000\n",
              "...        ...        ...        ...  ...       ...        ...     ...\n",
              "c-95   23814.0  -0.353726   1.629291  ...  -0.01025   0.444750   2.814\n",
              "c-96   23814.0  -0.463485   2.059725  ...  -0.00125   0.465225   3.505\n",
              "c-97   23814.0  -0.378241   1.703615  ...  -0.00680   0.446400   2.924\n",
              "c-98   23814.0  -0.470252   1.834828  ...   0.01400   0.461275   3.111\n",
              "c-99   23814.0  -0.301505   1.407918  ...  -0.01950   0.438650   3.805\n",
              "\n",
              "[873 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQ9BWpu7E1-J"
      },
      "source": [
        "#### Comentário sobre tendências centrais e dispersão\n",
        "\n",
        "> Com uma breve visualização da tabela acima podemos verificar a quantidade de amostras na coluna ``count``. Além disso temos a media (`mean`), desvio padrão amostral (`std`), os valores mínimo (`min`) / máximo (`max`) e os quartis 25%, 50% e 75%.\n",
        "\n",
        "> Note que para cada variável as médias e desvio são diferentes, e portanto, esses dados têm distribuições diferentes. Outro ponto é que os valores minimos e máximo são diferentes também para cada variável.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLau6hCkaB42"
      },
      "source": [
        "## Expressão gênica\n",
        "\n",
        "Expressão gênica é um assunto avançado de biologia molecular. Resuminadamente é um processo pelo qual caracteristicas são expressas nos seres vivos devido a fatores genéticos, isto é, informações contidas no DNA. O DNA é a base de dados de todo ser vivo, lá estão os \"tutoriais\" de como realizar atividades intracelulares.\n",
        "\n",
        "O processo mais importante desses \"tutoriais\" do DNA é a sintese de proteínas. Basicamente são os genes do DNA que fornecem a informação de como uma cadeia de aminoácidos devem ser construida por outra organelas da celula.\n",
        "\n",
        "<img src='https://upload.wikimedia.org/wikipedia/commons/b/bb/Dogma_central_-_traduzido.jpg' width=70%>\n",
        "\n",
        "> fonte: [Introdução à expressão gênica (dogma central)](https://pt.khanacademy.org/science/biology/gene-expression-central-dogma/central-dogma-transcription/a/intro-to-gene-expression-central-dogma)\n",
        "\n",
        "\n",
        "E como isso afeta as características dos seres vivos? Por exemplo, a cor de uma ervilha ou flor é determinada por um gene em seu DNA, isso porque o DNA tem instruções de como a célula deve produzir as proteinas que vão dar origens aos pigmentos da coloração da ervilha ou da flor, veja o exemplo abaixo\n",
        "\n",
        "<img src='https://cdn.kastatic.org/ka-perseus-images/53b7ece60303244264411d03bfbe55d36312b64e.png' width=70%>\n",
        "\n",
        "> fonte:[Introdução à expressão gênica (dogma central)](https://pt.khanacademy.org/science/biology/gene-expression-central-dogma/central-dogma-transcription/a/intro-to-gene-expression-central-dogma)\n",
        "\n",
        "Em condições bem definidas expressões gênicas de uma célula são bem definidas, mas dados alguns estímulos externos, internos ou farmacológicos a expressão gênica pode aumentar ou diminuir, isto é, aumentar a produção de proteinas ou outras atividades celulares.\n",
        "\n",
        "Dentro do dataset existem algumas variáveis relacionadas à expressão gênica, por exemplo a variável ``g-0``."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ixVumcrcvJq",
        "outputId": "01e7b931-9b2d-4e11-9694-098f3a5275b3"
      },
      "source": [
        "experimentos['g-0']"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1.0620\n",
              "1        0.0743\n",
              "2        0.6280\n",
              "3       -0.5138\n",
              "4       -0.3254\n",
              "          ...  \n",
              "23809    0.1394\n",
              "23810   -1.3260\n",
              "23811    0.3942\n",
              "23812    0.6660\n",
              "23813   -0.8598\n",
              "Name: g-0, Length: 23814, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pP83UbjWCtis"
      },
      "source": [
        "### Removendo hífen das variáveis do experimento\n",
        "\n",
        "Note que todas as colunas referentes às variaveis analisadas no experimento contém um hífen, vamos remover este caracter para facilitar a manipulação das variaveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZn59kukC_f3",
        "outputId": "4c6f403d-bf55-4cf7-f1f6-260d7b562faf"
      },
      "source": [
        "variaveis_sem_hifen = []\n",
        "\n",
        "for variavel in experimentos.columns:\n",
        "  if '-' in variavel:\n",
        "    variavel = variavel.replace('-','')\n",
        "    variaveis_sem_hifen.append(variavel)\n",
        "  else:\n",
        "    variaveis_sem_hifen.append(variavel)\n",
        "\n",
        "print(variaveis_sem_hifen)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['id', 'tratamento', 'tempo', 'dose', 'droga', 'g0', 'g1', 'g2', 'g3', 'g4', 'g5', 'g6', 'g7', 'g8', 'g9', 'g10', 'g11', 'g12', 'g13', 'g14', 'g15', 'g16', 'g17', 'g18', 'g19', 'g20', 'g21', 'g22', 'g23', 'g24', 'g25', 'g26', 'g27', 'g28', 'g29', 'g30', 'g31', 'g32', 'g33', 'g34', 'g35', 'g36', 'g37', 'g38', 'g39', 'g40', 'g41', 'g42', 'g43', 'g44', 'g45', 'g46', 'g47', 'g48', 'g49', 'g50', 'g51', 'g52', 'g53', 'g54', 'g55', 'g56', 'g57', 'g58', 'g59', 'g60', 'g61', 'g62', 'g63', 'g64', 'g65', 'g66', 'g67', 'g68', 'g69', 'g70', 'g71', 'g72', 'g73', 'g74', 'g75', 'g76', 'g77', 'g78', 'g79', 'g80', 'g81', 'g82', 'g83', 'g84', 'g85', 'g86', 'g87', 'g88', 'g89', 'g90', 'g91', 'g92', 'g93', 'g94', 'g95', 'g96', 'g97', 'g98', 'g99', 'g100', 'g101', 'g102', 'g103', 'g104', 'g105', 'g106', 'g107', 'g108', 'g109', 'g110', 'g111', 'g112', 'g113', 'g114', 'g115', 'g116', 'g117', 'g118', 'g119', 'g120', 'g121', 'g122', 'g123', 'g124', 'g125', 'g126', 'g127', 'g128', 'g129', 'g130', 'g131', 'g132', 'g133', 'g134', 'g135', 'g136', 'g137', 'g138', 'g139', 'g140', 'g141', 'g142', 'g143', 'g144', 'g145', 'g146', 'g147', 'g148', 'g149', 'g150', 'g151', 'g152', 'g153', 'g154', 'g155', 'g156', 'g157', 'g158', 'g159', 'g160', 'g161', 'g162', 'g163', 'g164', 'g165', 'g166', 'g167', 'g168', 'g169', 'g170', 'g171', 'g172', 'g173', 'g174', 'g175', 'g176', 'g177', 'g178', 'g179', 'g180', 'g181', 'g182', 'g183', 'g184', 'g185', 'g186', 'g187', 'g188', 'g189', 'g190', 'g191', 'g192', 'g193', 'g194', 'g195', 'g196', 'g197', 'g198', 'g199', 'g200', 'g201', 'g202', 'g203', 'g204', 'g205', 'g206', 'g207', 'g208', 'g209', 'g210', 'g211', 'g212', 'g213', 'g214', 'g215', 'g216', 'g217', 'g218', 'g219', 'g220', 'g221', 'g222', 'g223', 'g224', 'g225', 'g226', 'g227', 'g228', 'g229', 'g230', 'g231', 'g232', 'g233', 'g234', 'g235', 'g236', 'g237', 'g238', 'g239', 'g240', 'g241', 'g242', 'g243', 'g244', 'g245', 'g246', 'g247', 'g248', 'g249', 'g250', 'g251', 'g252', 'g253', 'g254', 'g255', 'g256', 'g257', 'g258', 'g259', 'g260', 'g261', 'g262', 'g263', 'g264', 'g265', 'g266', 'g267', 'g268', 'g269', 'g270', 'g271', 'g272', 'g273', 'g274', 'g275', 'g276', 'g277', 'g278', 'g279', 'g280', 'g281', 'g282', 'g283', 'g284', 'g285', 'g286', 'g287', 'g288', 'g289', 'g290', 'g291', 'g292', 'g293', 'g294', 'g295', 'g296', 'g297', 'g298', 'g299', 'g300', 'g301', 'g302', 'g303', 'g304', 'g305', 'g306', 'g307', 'g308', 'g309', 'g310', 'g311', 'g312', 'g313', 'g314', 'g315', 'g316', 'g317', 'g318', 'g319', 'g320', 'g321', 'g322', 'g323', 'g324', 'g325', 'g326', 'g327', 'g328', 'g329', 'g330', 'g331', 'g332', 'g333', 'g334', 'g335', 'g336', 'g337', 'g338', 'g339', 'g340', 'g341', 'g342', 'g343', 'g344', 'g345', 'g346', 'g347', 'g348', 'g349', 'g350', 'g351', 'g352', 'g353', 'g354', 'g355', 'g356', 'g357', 'g358', 'g359', 'g360', 'g361', 'g362', 'g363', 'g364', 'g365', 'g366', 'g367', 'g368', 'g369', 'g370', 'g371', 'g372', 'g373', 'g374', 'g375', 'g376', 'g377', 'g378', 'g379', 'g380', 'g381', 'g382', 'g383', 'g384', 'g385', 'g386', 'g387', 'g388', 'g389', 'g390', 'g391', 'g392', 'g393', 'g394', 'g395', 'g396', 'g397', 'g398', 'g399', 'g400', 'g401', 'g402', 'g403', 'g404', 'g405', 'g406', 'g407', 'g408', 'g409', 'g410', 'g411', 'g412', 'g413', 'g414', 'g415', 'g416', 'g417', 'g418', 'g419', 'g420', 'g421', 'g422', 'g423', 'g424', 'g425', 'g426', 'g427', 'g428', 'g429', 'g430', 'g431', 'g432', 'g433', 'g434', 'g435', 'g436', 'g437', 'g438', 'g439', 'g440', 'g441', 'g442', 'g443', 'g444', 'g445', 'g446', 'g447', 'g448', 'g449', 'g450', 'g451', 'g452', 'g453', 'g454', 'g455', 'g456', 'g457', 'g458', 'g459', 'g460', 'g461', 'g462', 'g463', 'g464', 'g465', 'g466', 'g467', 'g468', 'g469', 'g470', 'g471', 'g472', 'g473', 'g474', 'g475', 'g476', 'g477', 'g478', 'g479', 'g480', 'g481', 'g482', 'g483', 'g484', 'g485', 'g486', 'g487', 'g488', 'g489', 'g490', 'g491', 'g492', 'g493', 'g494', 'g495', 'g496', 'g497', 'g498', 'g499', 'g500', 'g501', 'g502', 'g503', 'g504', 'g505', 'g506', 'g507', 'g508', 'g509', 'g510', 'g511', 'g512', 'g513', 'g514', 'g515', 'g516', 'g517', 'g518', 'g519', 'g520', 'g521', 'g522', 'g523', 'g524', 'g525', 'g526', 'g527', 'g528', 'g529', 'g530', 'g531', 'g532', 'g533', 'g534', 'g535', 'g536', 'g537', 'g538', 'g539', 'g540', 'g541', 'g542', 'g543', 'g544', 'g545', 'g546', 'g547', 'g548', 'g549', 'g550', 'g551', 'g552', 'g553', 'g554', 'g555', 'g556', 'g557', 'g558', 'g559', 'g560', 'g561', 'g562', 'g563', 'g564', 'g565', 'g566', 'g567', 'g568', 'g569', 'g570', 'g571', 'g572', 'g573', 'g574', 'g575', 'g576', 'g577', 'g578', 'g579', 'g580', 'g581', 'g582', 'g583', 'g584', 'g585', 'g586', 'g587', 'g588', 'g589', 'g590', 'g591', 'g592', 'g593', 'g594', 'g595', 'g596', 'g597', 'g598', 'g599', 'g600', 'g601', 'g602', 'g603', 'g604', 'g605', 'g606', 'g607', 'g608', 'g609', 'g610', 'g611', 'g612', 'g613', 'g614', 'g615', 'g616', 'g617', 'g618', 'g619', 'g620', 'g621', 'g622', 'g623', 'g624', 'g625', 'g626', 'g627', 'g628', 'g629', 'g630', 'g631', 'g632', 'g633', 'g634', 'g635', 'g636', 'g637', 'g638', 'g639', 'g640', 'g641', 'g642', 'g643', 'g644', 'g645', 'g646', 'g647', 'g648', 'g649', 'g650', 'g651', 'g652', 'g653', 'g654', 'g655', 'g656', 'g657', 'g658', 'g659', 'g660', 'g661', 'g662', 'g663', 'g664', 'g665', 'g666', 'g667', 'g668', 'g669', 'g670', 'g671', 'g672', 'g673', 'g674', 'g675', 'g676', 'g677', 'g678', 'g679', 'g680', 'g681', 'g682', 'g683', 'g684', 'g685', 'g686', 'g687', 'g688', 'g689', 'g690', 'g691', 'g692', 'g693', 'g694', 'g695', 'g696', 'g697', 'g698', 'g699', 'g700', 'g701', 'g702', 'g703', 'g704', 'g705', 'g706', 'g707', 'g708', 'g709', 'g710', 'g711', 'g712', 'g713', 'g714', 'g715', 'g716', 'g717', 'g718', 'g719', 'g720', 'g721', 'g722', 'g723', 'g724', 'g725', 'g726', 'g727', 'g728', 'g729', 'g730', 'g731', 'g732', 'g733', 'g734', 'g735', 'g736', 'g737', 'g738', 'g739', 'g740', 'g741', 'g742', 'g743', 'g744', 'g745', 'g746', 'g747', 'g748', 'g749', 'g750', 'g751', 'g752', 'g753', 'g754', 'g755', 'g756', 'g757', 'g758', 'g759', 'g760', 'g761', 'g762', 'g763', 'g764', 'g765', 'g766', 'g767', 'g768', 'g769', 'g770', 'g771', 'c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9', 'c10', 'c11', 'c12', 'c13', 'c14', 'c15', 'c16', 'c17', 'c18', 'c19', 'c20', 'c21', 'c22', 'c23', 'c24', 'c25', 'c26', 'c27', 'c28', 'c29', 'c30', 'c31', 'c32', 'c33', 'c34', 'c35', 'c36', 'c37', 'c38', 'c39', 'c40', 'c41', 'c42', 'c43', 'c44', 'c45', 'c46', 'c47', 'c48', 'c49', 'c50', 'c51', 'c52', 'c53', 'c54', 'c55', 'c56', 'c57', 'c58', 'c59', 'c60', 'c61', 'c62', 'c63', 'c64', 'c65', 'c66', 'c67', 'c68', 'c69', 'c70', 'c71', 'c72', 'c73', 'c74', 'c75', 'c76', 'c77', 'c78', 'c79', 'c80', 'c81', 'c82', 'c83', 'c84', 'c85', 'c86', 'c87', 'c88', 'c89', 'c90', 'c91', 'c92', 'c93', 'c94', 'c95', 'c96', 'c97', 'c98', 'c99']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "vRd2txlyEsQO",
        "outputId": "7752d2d4-fb3f-4b0c-8507-ea12b3bd64f8"
      },
      "source": [
        "experimentos.columns = variaveis_sem_hifen\n",
        "experimentos.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
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              "      <th>g6</th>\n",
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              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
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              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
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              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>...</th>\n",
              "      <th>c60</th>\n",
              "      <th>c61</th>\n",
              "      <th>c62</th>\n",
              "      <th>c63</th>\n",
              "      <th>c64</th>\n",
              "      <th>c65</th>\n",
              "      <th>c66</th>\n",
              "      <th>c67</th>\n",
              "      <th>c68</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</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  ...     c96     c97     c98     c99\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FGOPnEtEo37"
      },
      "source": [
        "Assim retiramos os hifens das variaveis e fica mais facil trabalhar com as colunas do dataframe, chamando-as como se fossem atributos do objeto DataFrame."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wagZBWPuFAiH"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lu10WUtrFAnV"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCIU8n7p9D9G"
      },
      "source": [
        "---\n",
        "# Estatisticas, visualizações e distribuições\n",
        "\n",
        "## Resumo\n",
        "\n",
        "**completar**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLNZ2Y7-OjIN"
      },
      "source": [
        "## "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kR4FeSvVOblR"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eg_10y4d9FVH"
      },
      "source": [
        "# Aula 3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYvRd4Xq9HW8"
      },
      "source": [
        "# Aula 4\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGu1VUnF9Ib7"
      },
      "source": [
        "# Aula 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWs-B3ip9I2p"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    }
  ]
}