análise_drug_discovery.ipynb

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  "metadata": {
    "colab": {
      "name": "Cópia de Aula01.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPg6ny0I9u+NhTMH+3td9ot",
      "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/Bruno-Marin/ImersaoDados/blob/main/Aula01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZtOYbd4NmLB"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()\n",
        "\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "dados = pd.read_csv(url_dados, compression='zip')\n",
        "dados\n",
        "\n",
        "mapa = {'droga': 'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "4QzKofg6ZRYj",
        "outputId": "e93a2613-c394-432f-953b-8e8cde450327"
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
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              "      <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": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFx2cTd1Zmbc",
        "outputId": "e7cdafc6-842a-44ae-dea4-3d6eeea5fb97"
      },
      "source": [
        "dados.shape"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23814, 877)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nhOMlbP5zLb",
        "outputId": "2e8148db-5a27-495d-cb64-3dea95c15353"
      },
      "source": [
        "dados['tratamento']"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           com_droga\n",
              "1           com_droga\n",
              "2           com_droga\n",
              "3           com_droga\n",
              "4           com_droga\n",
              "             ...     \n",
              "23809       com_droga\n",
              "23810       com_droga\n",
              "23811    com_controle\n",
              "23812       com_droga\n",
              "23813       com_droga\n",
              "Name: tratamento, Length: 23814, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yleh7b0X56B1",
        "outputId": "e677f548-1536-4e41-beab-5cc343c65897"
      },
      "source": [
        "dados['tratamento'].unique()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['com_droga', 'com_controle'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOc6xEYJ6AA_",
        "outputId": "c58bfc61-1dae-457e-e81b-a33a38955c53"
      },
      "source": [
        "dados['tempo'].unique()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24, 72, 48])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYGirN9X6OmU",
        "outputId": "eb4ee9af-3a2b-4dc0-a12a-e7c0fb76a973"
      },
      "source": [
        "dados['dose'].unique()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['D1', 'D2'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsu3S_JS6lMV",
        "outputId": "bbbb1089-def2-4dd4-b262-fc81c3b4057d"
      },
      "source": [
        "dados['composto'].unique()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['b68db1d53', 'df89a8e5a', '18bb41b2c', ..., '573c787a2',\n",
              "       'b2fe3eca7', 'dd4a96d16'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kg7FHvdC60_K",
        "outputId": "b8902bda-e8d2-4a50-aaa8-161a8b475464"
      },
      "source": [
        "dados['g-0'].unique()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.062 ,  0.0743,  0.628 , ...,  0.3942,  0.666 , -0.8598])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiFYTWD47Uuq",
        "outputId": "0d4de7dc-07c6-40e2-b08b-17cca02b0450"
      },
      "source": [
        "dados['tratamento'].value_counts()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_droga       21948\n",
              "com_controle     1866\n",
              "Name: tratamento, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZOGKC3D-HAU",
        "outputId": "28e923a7-ef57-48d8-f8dc-d7966229f5f7"
      },
      "source": [
        "dados['dose'].value_counts()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "D1    12147\n",
              "D2    11667\n",
              "Name: dose, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuIs_Nxt-gJf",
        "outputId": "49d356bf-1d9a-4d29-a3e6-319ba04e31ab"
      },
      "source": [
        "dados['tratamento'].value_counts(normalize=True)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "com_droga       0.921643\n",
              "com_controle    0.078357\n",
              "Name: tratamento, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gY3uhGCk_uCz",
        "outputId": "7fbea337-b6ba-4757-c702-b28bf54bf4d8"
      },
      "source": [
        "dados['tempo'].value_counts(normalize=True)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48    0.346435\n",
              "72    0.327202\n",
              "24    0.326363\n",
              "Name: tempo, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSKyMzdj_Bf1",
        "outputId": "3de8837e-297d-4c35-9527-a96247096b38"
      },
      "source": [
        "dados['dose'].value_counts(normalize=True)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "D1    0.510078\n",
              "D2    0.489922\n",
              "Name: dose, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOc5baBzAKmL",
        "outputId": "38e7adfd-5bd3-45bb-e7d4-cadff887858a"
      },
      "source": [
        "dados['composto'].value_counts(normalize=True)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    0.078357\n",
              "87d714366    0.030150\n",
              "9f80f3f77    0.010330\n",
              "8b87a7a83    0.008524\n",
              "5628cb3ee    0.008482\n",
              "               ...   \n",
              "58d311643    0.000042\n",
              "d75904698    0.000042\n",
              "6e2b418c3    0.000042\n",
              "68282e3fb    0.000042\n",
              "f19d8c43b    0.000042\n",
              "Name: composto, Length: 3289, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "V29dhNYKDPHu",
        "outputId": "1e43bd28-9dbe-42bb-c202-356ba0664381"
      },
      "source": [
        "ax = plt.subplots(figsize = (8,6), dpi = 105)\n",
        "title = dados.keys()[1].upper()\n",
        "label = dados['tratamento'].unique()\n",
        "plt.pie(x=tratamento, labels=label, autopct=\"%.2f%%\")\n",
        "plt.title(title,fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 840x630 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "07-QD3iCDwus",
        "outputId": "7b483a1d-9be1-456d-f91a-af76ba4bd8a2"
      },
      "source": [
        "plt.figure(figsize=(8,6), dpi = 85)\n",
        "ax = sns.countplot(x='tempo', data=dados, palette=sns.color_palette('Set2'))\n",
        "ax.set_title('Duração do Tratamento\\n', fontsize=15)\n",
        "ax.set_xlabel('\\n Tempo (h)')\n",
        "ax.set_ylabel('Número de amostras testadas\\n ')\n",
        "plt.show()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 680x510 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EGvhGC1eEEv"
      },
      "source": [
        "composto = dados.composto.value_counts().index[:5]\n",
        "drogas_mais = dados.query('composto in @composto')"
      ],
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "GXdM6LlKdJFH",
        "outputId": "d913911d-f853-4009-f708-b475714248f3"
      },
      "source": [
        "plt.figure(figsize=(10,7), dpi=70)\n",
        "ax = sns.countplot(x = 'composto', data=drogas_mais, order = composto, palette=sns.color_palette('Set2'))\n",
        "ax.set_title('As Cinco drogas mais estudadas\\n'.upper(),fontsize= 18)\n",
        "ax.set_xlabel('\\nDrogas')\n",
        "ax.set_ylabel('Quantidade pesquisada\\n')\n",
        "plt.show()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x490 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "9L7gyKoqnzl6",
        "outputId": "d3856ed1-27f6-42bb-d40e-64f86b9ad324"
      },
      "source": [
        "coluna_analisar = 'g-0'\n",
        "data = dados[coluna_analisar]\n",
        "plt.figure(figsize=(10,8), dpi = 74)\n",
        "ax = sns.histplot(data=data, bins=100)\n",
        "ax.set_ylabel('Quantidade de drogas utilizadas no estudo\\n')\n",
        "ax.set_xlabel(f'\\nGene analisado: {coluna_analisar.upper()}')\n",
        "ax.set_title(f'Distribuição da expressão gênica do gene ({coluna_analisar}) frente a diferentes drogas\\n'.upper())\n",
        "plt.title('Distribuição da espressão gênica do gene (g-0)'.upper(),fontsize=15)\n",
        "plt.show()\n"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 740x592 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AN76zbQLn7c3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}