idd3_nb_d3.ipynb

{
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  "metadata": {
    "colab": {
      "name": "idd3_nb_d3.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPW5D0BFGCEPzoJHRl58nEh",
      "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/EloizioHMD/imersaodados3/blob/main/idd3_nb_d3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gcYMxUzv-sK"
      },
      "source": [
        "# Dia 3 da Imersão\n",
        "\n",
        "início ao notebook carregando algumas informações já passadas. Como muito das coisas que estou fazendo nessa imersão são novas, ajuda a reforçar o aprendizado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5w8n8ImytYJ"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBNdP6tGvhAP"
      },
      "source": [
        "url_dados = 'https://github.com/EloizioHMD/imersaodados3/raw/main/dados/dados_experimentos.zip'\n",
        "dados = pd.read_csv(url_dados, compression= \"zip\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "UsDjeztQyRxO",
        "outputId": "01d7a5d6-85e5-4ee8-aef3-ef54b9ad433a"
      },
      "source": [
        "mapa = {'droga' : 'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)\n",
        "dados"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>tratamento</th>\n",
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              "  </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",
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              "      <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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>0.4024</td>\n",
              "      <td>-0.0895</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1410</td>\n",
              "      <td>1.2640</td>\n",
              "      <td>-0.8663</td>\n",
              "      <td>0.8129</td>\n",
              "      <td>-0.1514</td>\n",
              "      <td>-0.4652</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.9170</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 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",
              "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",
              "[23814 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbMtNzhozMjd"
      },
      "source": [
        "A aula buscou trabalhar a partir dos dados as correlações, relações e causalidades entre genes. Inicialmente, assim como nos dias anteriores, seguir fazendo algumas exploração dos dados para reverar mais insight."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Adlg4QKqzucQ"
      },
      "source": [
        "## Tabela de frequência"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "hX7VCjIpzo5V",
        "outputId": "49006994-0a0a-4d1f-d120-422f3befeccc"
      },
      "source": [
        "pd.crosstab(dados['dose'], dados['tempo']) ## Relacionando o count entre duas colunas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>tempo</th>\n",
              "      <th>24</th>\n",
              "      <th>48</th>\n",
              "      <th>72</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>D1</th>\n",
              "      <td>3886</td>\n",
              "      <td>4354</td>\n",
              "      <td>3907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>3886</td>\n",
              "      <td>3896</td>\n",
              "      <td>3885</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tempo    24    48    72\n",
              "dose                   \n",
              "D1     3886  4354  3907\n",
              "D2     3886  3896  3885"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "c7BDrsM51URq",
        "outputId": "b9a921f0-6ea0-49ba-fd77-802419726152"
      },
      "source": [
        "## É possível fazer o mesmo para mais de uma coluna, bastando agregar duas delas em um único objeto com []\n",
        "pd.crosstab([dados['dose'], dados['tempo']],dados['tratamento']) "
      ],
      "execution_count": null,
      "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",
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              "    }\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>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>301</td>\n",
              "      <td>3585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>343</td>\n",
              "      <td>4011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>307</td>\n",
              "      <td>3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>305</td>\n",
              "      <td>3581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>305</td>\n",
              "      <td>3591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>305</td>\n",
              "      <td>3580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24              301       3585\n",
              "     48              343       4011\n",
              "     72              307       3600\n",
              "D2   24              305       3581\n",
              "     48              305       3591\n",
              "     72              305       3580"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "9rQYYrPj1xkZ",
        "outputId": "c83988db-9a30-4fa0-9344-52ec8db15cc2"
      },
      "source": [
        "pd.crosstab([dados['dose'], dados['tempo']],dados['tratamento'], normalize=True) ## obter proporção global basta normalizar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.012640</td>\n",
              "      <td>0.150542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.014403</td>\n",
              "      <td>0.168430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.012892</td>\n",
              "      <td>0.151172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.012808</td>\n",
              "      <td>0.150374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.012808</td>\n",
              "      <td>0.150794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.012808</td>\n",
              "      <td>0.150332</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.012640   0.150542\n",
              "     48         0.014403   0.168430\n",
              "     72         0.012892   0.151172\n",
              "D2   24         0.012808   0.150374\n",
              "     48         0.012808   0.150794\n",
              "     72         0.012808   0.150332"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "xRcPXBl12Kkj",
        "outputId": "f501e264-2dd8-4c8c-bd76-98278ddb4743"
      },
      "source": [
        "pd.crosstab([dados['dose'], dados['tempo']],dados['tratamento'], normalize='index') ## é possível normalizarpor linha com index"
      ],
      "execution_count": null,
      "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 thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.077458</td>\n",
              "      <td>0.922542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078778</td>\n",
              "      <td>0.921222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078577</td>\n",
              "      <td>0.921423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.078487</td>\n",
              "      <td>0.921513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.078285</td>\n",
              "      <td>0.921715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.078507</td>\n",
              "      <td>0.921493</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.077458   0.922542\n",
              "     48         0.078778   0.921222\n",
              "     72         0.078577   0.921423\n",
              "D2   24         0.078487   0.921513\n",
              "     48         0.078285   0.921715\n",
              "     72         0.078507   0.921493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpAAjJqk4k6b"
      },
      "source": [
        "Como estamos trabalhando com tabelas, a biblioteca `NumPy` entrega algumas funcionalidades por meio dos agregadores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "JLbMQUXl4z-i",
        "outputId": "4ca2c9b6-446d-47c4-d2b0-0af36a5540fc"
      },
      "source": [
        "pd.crosstab([dados['dose'], dados['tempo']],  dados['tratamento'], values=dados['g-0'], aggfunc='mean')"
      ],
      "execution_count": null,
      "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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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>-0.251760</td>\n",
              "      <td>0.367996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>-0.122773</td>\n",
              "      <td>0.412469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>-0.295271</td>\n",
              "      <td>0.286250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.010754</td>\n",
              "      <td>0.115253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.013174</td>\n",
              "      <td>0.214480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.070250</td>\n",
              "      <td>0.253439</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24        -0.251760   0.367996\n",
              "     48        -0.122773   0.412469\n",
              "     72        -0.295271   0.286250\n",
              "D2   24         0.010754   0.115253\n",
              "     48         0.013174   0.214480\n",
              "     72         0.070250   0.253439"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kglc_hU5qoQ"
      },
      "source": [
        "## Scatterplot\n",
        "\n",
        "Outra forma interessante de explorar correlação é através do gráfico [`scatterplot`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html). Iremos usar esse recurso entre gene para entender a correlação entre eles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "cocyxvBC55_K",
        "outputId": "3c648e71-a228-4bc4-e318-299a4599f13e"
      },
      "source": [
        "dados[['g-0', 'g-10']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0620</td>\n",
              "      <td>1.1830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0743</td>\n",
              "      <td>-1.1520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6280</td>\n",
              "      <td>-0.4797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.8269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.2219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      g-0    g-10\n",
              "0  1.0620  1.1830\n",
              "1  0.0743 -1.1520\n",
              "2  0.6280 -0.4797\n",
              "3 -0.5138 -0.8269\n",
              "4 -0.3254 -0.2219"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "mSByoNRN6aNc",
        "outputId": "84c3f7e6-c05c-4088-c6d9-941b558ff3c8"
      },
      "source": [
        "sns.scatterplot(x='g-0', y='g-10', data=dados)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74ea01c190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1V5ROAu7W7e"
      },
      "source": [
        "É possível usar o `hue=` para pegar uma variável de agrupamento produzindo pontos com cores diferentes para diferenciação. Pode ser categórico ou numérico, embora o mapeamento de cores se comporte de maneira diferente neste último caso."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "QLHAfPil6i7X",
        "outputId": "9ca1dba3-9751-41bc-e902-447a11987147"
      },
      "source": [
        "sns.scatterplot(x='g-0', y='g-10', data=dados, hue='tempo')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74e9cf0a10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-tH9R3O8Amy"
      },
      "source": [
        "Similar ao scatterplot o seaborn possuí o [lmplot](https://seaborn.pydata.org/generated/seaborn.lmplot.html), que além do gráfico de dispersão, vai desenhar uma linha de tendência e, assim, poderemos confirmar o padrão daquele conjunto de dados em análise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "ShaHhDAd8WQW",
        "outputId": "34eb115f-b9c2-4b88-f8bf-c5d40d1a7e90"
      },
      "source": [
        "sns.lmplot(data=dados, x='g-0', y='g-10', hue='tratamento', line_kws={'color': 'red'}) ## o line_kws serve para mudar a cor da linha."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f74e8d30f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 474.1x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0XU60tU83JF"
      },
      "source": [
        "Essa ferramenta é muito poderosa, é possível inclusive selecionar parâmetros para correlacionar os gráficos. Exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vck3j-0N9GvN",
        "outputId": "fcc366de-1bf3-422b-fc65-1e9c8c87522d"
      },
      "source": [
        "sns.lmplot(data=dados, x='g-0', y='g-8', line_kws={'color': 'red'}, col='tratamento', row='tempo')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f74e8d34bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L3jcX6Sg9lzA"
      },
      "source": [
        "## Correlação e Heatmap\n",
        "\n",
        "Uma das coisas mais interessantes no Python é a quantidade de códigos que podem ser aproveitas gerado pela comunidade. É o que iremos fazer no passo seguinte.\n",
        "\n",
        "Iremos correlacionar alguns dados dos genes e posteriormente iremos produzir uma mapa de calor com essa correlação aproveitando um código fornecido na documentação do seaborn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "gU9Groiv9_sm",
        "outputId": "adfdf83c-3e25-417f-d35a-198b0b1333ee"
      },
      "source": [
        "dados.loc[:,'g-0':'g-771'].corr().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>g-36</th>\n",
              "      <th>g-37</th>\n",
              "      <th>g-38</th>\n",
              "      <th>g-39</th>\n",
              "      <th>...</th>\n",
              "      <th>g-732</th>\n",
              "      <th>g-733</th>\n",
              "      <th>g-734</th>\n",
              "      <th>g-735</th>\n",
              "      <th>g-736</th>\n",
              "      <th>g-737</th>\n",
              "      <th>g-738</th>\n",
              "      <th>g-739</th>\n",
              "      <th>g-740</th>\n",
              "      <th>g-741</th>\n",
              "      <th>g-742</th>\n",
              "      <th>g-743</th>\n",
              "      <th>g-744</th>\n",
              "      <th>g-745</th>\n",
              "      <th>g-746</th>\n",
              "      <th>g-747</th>\n",
              "      <th>g-748</th>\n",
              "      <th>g-749</th>\n",
              "      <th>g-750</th>\n",
              "      <th>g-751</th>\n",
              "      <th>g-752</th>\n",
              "      <th>g-753</th>\n",
              "      <th>g-754</th>\n",
              "      <th>g-755</th>\n",
              "      <th>g-756</th>\n",
              "      <th>g-757</th>\n",
              "      <th>g-758</th>\n",
              "      <th>g-759</th>\n",
              "      <th>g-760</th>\n",
              "      <th>g-761</th>\n",
              "      <th>g-762</th>\n",
              "      <th>g-763</th>\n",
              "      <th>g-764</th>\n",
              "      <th>g-765</th>\n",
              "      <th>g-766</th>\n",
              "      <th>g-767</th>\n",
              "      <th>g-768</th>\n",
              "      <th>g-769</th>\n",
              "      <th>g-770</th>\n",
              "      <th>g-771</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g-0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.086032</td>\n",
              "      <td>0.176188</td>\n",
              "      <td>0.011266</td>\n",
              "      <td>0.403153</td>\n",
              "      <td>-0.165478</td>\n",
              "      <td>0.238348</td>\n",
              "      <td>-0.374451</td>\n",
              "      <td>-0.604212</td>\n",
              "      <td>-0.428779</td>\n",
              "      <td>-0.547606</td>\n",
              "      <td>0.219762</td>\n",
              "      <td>-0.199453</td>\n",
              "      <td>0.472223</td>\n",
              "      <td>-0.346179</td>\n",
              "      <td>-0.201323</td>\n",
              "      <td>-0.513026</td>\n",
              "      <td>-0.433536</td>\n",
              "      <td>-0.002073</td>\n",
              "      <td>-0.074622</td>\n",
              "      <td>-0.468599</td>\n",
              "      <td>0.052445</td>\n",
              "      <td>0.022269</td>\n",
              "      <td>-0.076298</td>\n",
              "      <td>-0.337522</td>\n",
              "      <td>-0.235191</td>\n",
              "      <td>-0.422814</td>\n",
              "      <td>0.148432</td>\n",
              "      <td>-0.471092</td>\n",
              "      <td>-0.296871</td>\n",
              "      <td>-0.528327</td>\n",
              "      <td>0.558136</td>\n",
              "      <td>-0.133229</td>\n",
              "      <td>-0.155987</td>\n",
              "      <td>-0.120247</td>\n",
              "      <td>0.327593</td>\n",
              "      <td>-0.147800</td>\n",
              "      <td>-0.591868</td>\n",
              "      <td>0.602623</td>\n",
              "      <td>0.450769</td>\n",
              "      <td>...</td>\n",
              "      <td>0.365095</td>\n",
              "      <td>-0.528006</td>\n",
              "      <td>0.295970</td>\n",
              "      <td>0.067108</td>\n",
              "      <td>-0.250243</td>\n",
              "      <td>-0.110665</td>\n",
              "      <td>-0.214344</td>\n",
              "      <td>-0.280492</td>\n",
              "      <td>-0.036098</td>\n",
              "      <td>-0.128288</td>\n",
              "      <td>-0.498922</td>\n",
              "      <td>-0.469595</td>\n",
              "      <td>0.673664</td>\n",
              "      <td>0.538050</td>\n",
              "      <td>0.297528</td>\n",
              "      <td>0.066530</td>\n",
              "      <td>0.240252</td>\n",
              "      <td>-0.170949</td>\n",
              "      <td>0.485924</td>\n",
              "      <td>-0.093815</td>\n",
              "      <td>0.397476</td>\n",
              "      <td>0.017923</td>\n",
              "      <td>0.321260</td>\n",
              "      <td>-0.131709</td>\n",
              "      <td>-0.133430</td>\n",
              "      <td>0.178220</td>\n",
              "      <td>0.201902</td>\n",
              "      <td>-0.205648</td>\n",
              "      <td>-0.676835</td>\n",
              "      <td>-0.433554</td>\n",
              "      <td>0.005085</td>\n",
              "      <td>-0.239410</td>\n",
              "      <td>0.548401</td>\n",
              "      <td>0.425222</td>\n",
              "      <td>-0.308095</td>\n",
              "      <td>-0.052622</td>\n",
              "      <td>0.300241</td>\n",
              "      <td>-0.127375</td>\n",
              "      <td>-0.514201</td>\n",
              "      <td>0.405908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-1</th>\n",
              "      <td>0.086032</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054518</td>\n",
              "      <td>-0.027855</td>\n",
              "      <td>0.193647</td>\n",
              "      <td>-0.151590</td>\n",
              "      <td>-0.122541</td>\n",
              "      <td>0.144537</td>\n",
              "      <td>-0.009973</td>\n",
              "      <td>-0.047558</td>\n",
              "      <td>-0.009051</td>\n",
              "      <td>0.161274</td>\n",
              "      <td>0.037178</td>\n",
              "      <td>-0.009256</td>\n",
              "      <td>0.002632</td>\n",
              "      <td>-0.070193</td>\n",
              "      <td>-0.063414</td>\n",
              "      <td>-0.000944</td>\n",
              "      <td>-0.023748</td>\n",
              "      <td>-0.034714</td>\n",
              "      <td>-0.131031</td>\n",
              "      <td>0.196596</td>\n",
              "      <td>-0.061441</td>\n",
              "      <td>0.090620</td>\n",
              "      <td>-0.004299</td>\n",
              "      <td>-0.032600</td>\n",
              "      <td>-0.019435</td>\n",
              "      <td>0.015661</td>\n",
              "      <td>-0.098907</td>\n",
              "      <td>-0.091071</td>\n",
              "      <td>-0.053331</td>\n",
              "      <td>-0.049514</td>\n",
              "      <td>0.187382</td>\n",
              "      <td>-0.091232</td>\n",
              "      <td>0.045417</td>\n",
              "      <td>0.044373</td>\n",
              "      <td>-0.077671</td>\n",
              "      <td>-0.005265</td>\n",
              "      <td>-0.042213</td>\n",
              "      <td>0.050546</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.015556</td>\n",
              "      <td>-0.017184</td>\n",
              "      <td>0.043104</td>\n",
              "      <td>-0.189662</td>\n",
              "      <td>-0.050194</td>\n",
              "      <td>-0.007878</td>\n",
              "      <td>-0.083539</td>\n",
              "      <td>-0.042175</td>\n",
              "      <td>0.014307</td>\n",
              "      <td>-0.134166</td>\n",
              "      <td>-0.026985</td>\n",
              "      <td>0.088382</td>\n",
              "      <td>-0.077453</td>\n",
              "      <td>0.015545</td>\n",
              "      <td>-0.027527</td>\n",
              "      <td>-0.071078</td>\n",
              "      <td>-0.095445</td>\n",
              "      <td>0.049612</td>\n",
              "      <td>-0.040518</td>\n",
              "      <td>-0.074277</td>\n",
              "      <td>-0.022356</td>\n",
              "      <td>0.125763</td>\n",
              "      <td>-0.082779</td>\n",
              "      <td>0.020292</td>\n",
              "      <td>-0.023171</td>\n",
              "      <td>-0.207192</td>\n",
              "      <td>-0.066570</td>\n",
              "      <td>-0.057007</td>\n",
              "      <td>0.002062</td>\n",
              "      <td>0.029795</td>\n",
              "      <td>-0.205998</td>\n",
              "      <td>0.060051</td>\n",
              "      <td>0.115071</td>\n",
              "      <td>0.160140</td>\n",
              "      <td>0.015098</td>\n",
              "      <td>-0.003378</td>\n",
              "      <td>-0.030756</td>\n",
              "      <td>-0.006866</td>\n",
              "      <td>-0.095108</td>\n",
              "      <td>-0.032233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-2</th>\n",
              "      <td>0.176188</td>\n",
              "      <td>-0.054518</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042633</td>\n",
              "      <td>0.013968</td>\n",
              "      <td>0.018516</td>\n",
              "      <td>0.015190</td>\n",
              "      <td>-0.144205</td>\n",
              "      <td>-0.128029</td>\n",
              "      <td>-0.081332</td>\n",
              "      <td>-0.105050</td>\n",
              "      <td>0.011198</td>\n",
              "      <td>-0.063501</td>\n",
              "      <td>0.082011</td>\n",
              "      <td>-0.082204</td>\n",
              "      <td>-0.059607</td>\n",
              "      <td>-0.137811</td>\n",
              "      <td>-0.085491</td>\n",
              "      <td>0.053335</td>\n",
              "      <td>0.028813</td>\n",
              "      <td>-0.061160</td>\n",
              "      <td>-0.129131</td>\n",
              "      <td>-0.007426</td>\n",
              "      <td>-0.034230</td>\n",
              "      <td>-0.128312</td>\n",
              "      <td>-0.067363</td>\n",
              "      <td>-0.126554</td>\n",
              "      <td>0.036984</td>\n",
              "      <td>-0.129481</td>\n",
              "      <td>-0.017941</td>\n",
              "      <td>-0.152288</td>\n",
              "      <td>0.172191</td>\n",
              "      <td>-0.100086</td>\n",
              "      <td>-0.033010</td>\n",
              "      <td>-0.015481</td>\n",
              "      <td>0.001098</td>\n",
              "      <td>-0.020374</td>\n",
              "      <td>-0.157977</td>\n",
              "      <td>0.169464</td>\n",
              "      <td>0.144449</td>\n",
              "      <td>...</td>\n",
              "      <td>0.089525</td>\n",
              "      <td>-0.140916</td>\n",
              "      <td>0.008587</td>\n",
              "      <td>0.042106</td>\n",
              "      <td>-0.018846</td>\n",
              "      <td>-0.008093</td>\n",
              "      <td>-0.074404</td>\n",
              "      <td>-0.080120</td>\n",
              "      <td>-0.020085</td>\n",
              "      <td>-0.028099</td>\n",
              "      <td>-0.098775</td>\n",
              "      <td>-0.113000</td>\n",
              "      <td>0.175916</td>\n",
              "      <td>0.119166</td>\n",
              "      <td>0.071051</td>\n",
              "      <td>0.025358</td>\n",
              "      <td>0.078971</td>\n",
              "      <td>-0.086326</td>\n",
              "      <td>0.149456</td>\n",
              "      <td>0.033432</td>\n",
              "      <td>0.124239</td>\n",
              "      <td>-0.006327</td>\n",
              "      <td>0.120148</td>\n",
              "      <td>-0.041162</td>\n",
              "      <td>-0.030040</td>\n",
              "      <td>0.048549</td>\n",
              "      <td>0.096369</td>\n",
              "      <td>0.000494</td>\n",
              "      <td>-0.172627</td>\n",
              "      <td>-0.130200</td>\n",
              "      <td>0.090115</td>\n",
              "      <td>-0.055655</td>\n",
              "      <td>0.138085</td>\n",
              "      <td>0.061574</td>\n",
              "      <td>-0.093631</td>\n",
              "      <td>-0.053149</td>\n",
              "      <td>0.115415</td>\n",
              "      <td>0.014489</td>\n",
              "      <td>-0.078661</td>\n",
              "      <td>0.103985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-3</th>\n",
              "      <td>0.011266</td>\n",
              "      <td>-0.027855</td>\n",
              "      <td>0.042633</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.033039</td>\n",
              "      <td>0.042231</td>\n",
              "      <td>-0.043177</td>\n",
              "      <td>-0.175692</td>\n",
              "      <td>-0.086167</td>\n",
              "      <td>-0.014052</td>\n",
              "      <td>-0.074101</td>\n",
              "      <td>0.181024</td>\n",
              "      <td>0.030539</td>\n",
              "      <td>0.192727</td>\n",
              "      <td>0.128792</td>\n",
              "      <td>-0.045688</td>\n",
              "      <td>0.033974</td>\n",
              "      <td>-0.070920</td>\n",
              "      <td>0.237669</td>\n",
              "      <td>0.112392</td>\n",
              "      <td>0.027867</td>\n",
              "      <td>-0.167263</td>\n",
              "      <td>0.197013</td>\n",
              "      <td>-0.049480</td>\n",
              "      <td>-0.113336</td>\n",
              "      <td>0.175615</td>\n",
              "      <td>0.111355</td>\n",
              "      <td>0.007168</td>\n",
              "      <td>-0.022381</td>\n",
              "      <td>-0.128269</td>\n",
              "      <td>-0.042827</td>\n",
              "      <td>0.098416</td>\n",
              "      <td>-0.091962</td>\n",
              "      <td>-0.083806</td>\n",
              "      <td>0.184985</td>\n",
              "      <td>-0.070933</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>-0.228039</td>\n",
              "      <td>0.084346</td>\n",
              "      <td>0.054827</td>\n",
              "      <td>...</td>\n",
              "      <td>0.039519</td>\n",
              "      <td>0.000851</td>\n",
              "      <td>0.011655</td>\n",
              "      <td>-0.116724</td>\n",
              "      <td>0.304819</td>\n",
              "      <td>-0.087159</td>\n",
              "      <td>-0.134135</td>\n",
              "      <td>-0.031211</td>\n",
              "      <td>0.174116</td>\n",
              "      <td>0.060289</td>\n",
              "      <td>-0.119276</td>\n",
              "      <td>-0.140391</td>\n",
              "      <td>0.110547</td>\n",
              "      <td>0.145925</td>\n",
              "      <td>0.155011</td>\n",
              "      <td>0.049182</td>\n",
              "      <td>-0.007754</td>\n",
              "      <td>-0.128488</td>\n",
              "      <td>0.078139</td>\n",
              "      <td>0.012422</td>\n",
              "      <td>0.040728</td>\n",
              "      <td>0.164467</td>\n",
              "      <td>0.171886</td>\n",
              "      <td>-0.069777</td>\n",
              "      <td>0.066560</td>\n",
              "      <td>0.102111</td>\n",
              "      <td>-0.036922</td>\n",
              "      <td>0.111994</td>\n",
              "      <td>-0.133740</td>\n",
              "      <td>-0.226928</td>\n",
              "      <td>0.075982</td>\n",
              "      <td>0.178422</td>\n",
              "      <td>0.016467</td>\n",
              "      <td>-0.089317</td>\n",
              "      <td>-0.166426</td>\n",
              "      <td>-0.179370</td>\n",
              "      <td>0.028452</td>\n",
              "      <td>-0.206077</td>\n",
              "      <td>-0.002142</td>\n",
              "      <td>0.058219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-4</th>\n",
              "      <td>0.403153</td>\n",
              "      <td>0.193647</td>\n",
              "      <td>0.013968</td>\n",
              "      <td>-0.033039</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.057449</td>\n",
              "      <td>0.206405</td>\n",
              "      <td>-0.032748</td>\n",
              "      <td>-0.474828</td>\n",
              "      <td>-0.274167</td>\n",
              "      <td>-0.458021</td>\n",
              "      <td>0.116467</td>\n",
              "      <td>-0.207078</td>\n",
              "      <td>0.210440</td>\n",
              "      <td>-0.307280</td>\n",
              "      <td>0.019531</td>\n",
              "      <td>-0.275700</td>\n",
              "      <td>-0.422889</td>\n",
              "      <td>-0.012207</td>\n",
              "      <td>-0.088152</td>\n",
              "      <td>-0.311798</td>\n",
              "      <td>0.286106</td>\n",
              "      <td>-0.055368</td>\n",
              "      <td>-0.113396</td>\n",
              "      <td>-0.120743</td>\n",
              "      <td>-0.233682</td>\n",
              "      <td>-0.340708</td>\n",
              "      <td>0.070292</td>\n",
              "      <td>-0.360274</td>\n",
              "      <td>-0.225461</td>\n",
              "      <td>-0.402256</td>\n",
              "      <td>0.324362</td>\n",
              "      <td>0.091731</td>\n",
              "      <td>-0.052787</td>\n",
              "      <td>-0.073490</td>\n",
              "      <td>0.195062</td>\n",
              "      <td>-0.124394</td>\n",
              "      <td>-0.277002</td>\n",
              "      <td>0.285256</td>\n",
              "      <td>0.300517</td>\n",
              "      <td>...</td>\n",
              "      <td>0.215930</td>\n",
              "      <td>-0.292602</td>\n",
              "      <td>-0.008241</td>\n",
              "      <td>-0.005070</td>\n",
              "      <td>-0.284391</td>\n",
              "      <td>-0.116918</td>\n",
              "      <td>-0.252399</td>\n",
              "      <td>-0.250280</td>\n",
              "      <td>0.000141</td>\n",
              "      <td>-0.172896</td>\n",
              "      <td>-0.324769</td>\n",
              "      <td>-0.291644</td>\n",
              "      <td>0.302912</td>\n",
              "      <td>0.222951</td>\n",
              "      <td>0.144028</td>\n",
              "      <td>-0.020950</td>\n",
              "      <td>0.129354</td>\n",
              "      <td>-0.141319</td>\n",
              "      <td>0.215713</td>\n",
              "      <td>-0.060261</td>\n",
              "      <td>0.240649</td>\n",
              "      <td>-0.250950</td>\n",
              "      <td>0.144547</td>\n",
              "      <td>-0.045769</td>\n",
              "      <td>-0.083932</td>\n",
              "      <td>-0.100498</td>\n",
              "      <td>0.118725</td>\n",
              "      <td>-0.257704</td>\n",
              "      <td>-0.386764</td>\n",
              "      <td>-0.172222</td>\n",
              "      <td>-0.023752</td>\n",
              "      <td>-0.164868</td>\n",
              "      <td>0.404440</td>\n",
              "      <td>0.317500</td>\n",
              "      <td>-0.225094</td>\n",
              "      <td>0.124425</td>\n",
              "      <td>0.115579</td>\n",
              "      <td>-0.172727</td>\n",
              "      <td>-0.363673</td>\n",
              "      <td>0.250713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 772 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-0       g-1       g-2  ...     g-769     g-770     g-771\n",
              "g-0  1.000000  0.086032  0.176188  ... -0.127375 -0.514201  0.405908\n",
              "g-1  0.086032  1.000000 -0.054518  ... -0.006866 -0.095108 -0.032233\n",
              "g-2  0.176188 -0.054518  1.000000  ...  0.014489 -0.078661  0.103985\n",
              "g-3  0.011266 -0.027855  0.042633  ... -0.206077 -0.002142  0.058219\n",
              "g-4  0.403153  0.193647  0.013968  ... -0.172727 -0.363673  0.250713\n",
              "\n",
              "[5 rows x 772 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqeatJqmBVNy"
      },
      "source": [
        "corr = dados.loc[:,'g-0':'g-50'].corr() ## Atribuir um recorte do dado correlacioando à um objeto. "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8smJJbVXBvn2"
      },
      "source": [
        "Buscar na comunidade o [Plotting a diagonal correlation matrix](https://seaborn.pydata.org/examples/many_pairwise_correlations.html). *Mutatis mutandis* temos o seguinte:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "tu1038A-CDuQ",
        "outputId": "723761ae-3f83-45e1-ce31-11a30b4b85ac"
      },
      "source": [
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74fc1efb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YVxWUagCjJA"
      },
      "source": [
        "Esse gráfico demonstra por meio do recurso das cores o grau de correlação. Quanto mais próximo de 1 ou -1 maior a correlação, que será respectivamente positiva e negativa (em tons de laranja mais escuro e azul mais escuro). Uma correlação próxima de 0 existe pouca correlação, por isso o tom mais ameno das cores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRV31FcqDX4p"
      },
      "source": [
        "corr_celular = dados.loc[:,'c-0':'c-50'].corr() ## Corelacionando os dados de viabilidade celular (C)."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "dWayN_KRDqKU",
        "outputId": "7afe60c2-c2cb-49d3-809b-026c21e552d7"
      },
      "source": [
        "# Generate a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr_celular, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_celular, mask=mask, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74de8e1610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90NMulbrD3yo"
      },
      "source": [
        "Fica claro a correlação positiva entre as amostras de viabilidade celular."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdagB8jPEGmj"
      },
      "source": [
        "### Desafio 01\n",
        "Criar tabela e frequência com o `pandas.groupby()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "H0lYe62bEYcb",
        "outputId": "708c8327-ea8c-401e-f133-45d52b9f36f0"
      },
      "source": [
        "dados.groupby(by=['dose', 'tempo', 'tratamento']).nunique()[['composto']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>composto</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th>tratamento</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">D1</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">48</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">72</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">D2</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">48</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">72</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3135</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         composto\n",
              "dose tempo tratamento            \n",
              "D1   24    com_controle         1\n",
              "           com_droga         3141\n",
              "     48    com_controle         1\n",
              "           com_droga         3281\n",
              "     72    com_controle         1\n",
              "           com_droga         3147\n",
              "D2   24    com_controle         1\n",
              "           com_droga         3145\n",
              "     48    com_controle         1\n",
              "           com_droga         3140\n",
              "     72    com_controle         1\n",
              "           com_droga         3135"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "mrbj5_HFEpuH",
        "outputId": "13d67fed-403a-4b4b-bc62-b6dc6dd2bbe5"
      },
      "source": [
        "dados.groupby(by=['dose', 'tempo', 'tratamento']).count()[['composto']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>composto</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th>tratamento</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">D1</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">48</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>4011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">72</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">D2</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">24</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">48</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">72</th>\n",
              "      <th>com_controle</th>\n",
              "      <td>305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>com_droga</th>\n",
              "      <td>3580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         composto\n",
              "dose tempo tratamento            \n",
              "D1   24    com_controle       301\n",
              "           com_droga         3585\n",
              "     48    com_controle       343\n",
              "           com_droga         4011\n",
              "     72    com_controle       307\n",
              "           com_droga         3600\n",
              "D2   24    com_controle       305\n",
              "           com_droga         3581\n",
              "     48    com_controle       305\n",
              "           com_droga         3591\n",
              "     72    com_controle       305\n",
              "           com_droga         3580"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mXO574mH1V2"
      },
      "source": [
        "### Desafio 02\n",
        "Normalizar os dados pela coluna."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "nJrJZI4VH59C",
        "outputId": "e82f2527-68e6-4ec1-8263-e5f941bb1fea"
      },
      "source": [
        "pd.crosstab([dados['dose'], dados['tempo']],dados['tratamento'], normalize='columns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tratamento</th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D1</th>\n",
              "      <th>24</th>\n",
              "      <td>0.161308</td>\n",
              "      <td>0.163341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.183816</td>\n",
              "      <td>0.182750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.164523</td>\n",
              "      <td>0.164024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">D2</th>\n",
              "      <th>24</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.163451</td>\n",
              "      <td>0.163113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "tratamento  com_controle  com_droga\n",
              "dose tempo                         \n",
              "D1   24         0.161308   0.163341\n",
              "     48         0.183816   0.182750\n",
              "     72         0.164523   0.164024\n",
              "D2   24         0.163451   0.163158\n",
              "     48         0.163451   0.163614\n",
              "     72         0.163451   0.163113"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNz7qfETKyYL"
      },
      "source": [
        "### Desafio 03\n",
        "Explorar outros agregadores (Quais são os agregadores?)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lECuOTtuL5Cw"
      },
      "source": [
        "O Numpy possui agregação e estatísticas incorporadas rápidas para trabalhar em matrizes. Os agregadores são:\n",
        "* np.sum() : usado para descobrir a soma da matriz fornecida.\n",
        "* np.prod() : Usado para descobrir o produto (multiplicação) dos valores.\n",
        "* np.mean() : Retorna a média do array de entrada.\n",
        "* np.std() : Retorna o desvio padrão da matriz de entrada m fornecida.\n",
        "* np.var() : Usado para descobrir a variação dos dados fornecidos na forma de array.\n",
        "* np.min() : Retorna o valor mínimo entre os elementos do array m dado.\n",
        "* np.max() : Retorna o valor máximo entre os elementos do array m dado.\n",
        "* np.argmin() : retorna o índice do valor mínimo entre os elementos do array.\n",
        "* np.argmax() : retorna o índice do valor máximo entre os elementos do array.\n",
        "* np.median() : retorna a mediana dos elementos do array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjuwwUvGKzjC",
        "outputId": "a1ff7255-943a-4bfa-bea7-ee703d9cd58d"
      },
      "source": [
        "## Loop para gerar crosstab dos agregadores e comparar.\n",
        "\n",
        "mask=['sum', 'prod', 'mean', 'std', 'var', 'min', 'max', 'median']\n",
        "for func in mask:\n",
        "  print('\\n', func)\n",
        "  print(pd.crosstab(index  = [dados.dose, dados.tempo], \n",
        "                    columns= dados.tratamento, \n",
        "                    values = dados['g-0'], aggfunc=func))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " sum\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24         -75.7799  1319.2648\n",
            "     48         -42.1113  1654.4150\n",
            "     72         -90.6483  1030.5010\n",
            "D2   24           3.2800   412.7202\n",
            "     48           4.0180   770.1980\n",
            "     72          21.4262   907.3112\n",
            "\n",
            " prod\n",
            "tratamento   com_controle  com_droga\n",
            "dose tempo                          \n",
            "D1   24     9.143747e-120       -0.0\n",
            "     48    -8.360749e-127        0.0\n",
            "     72     -2.183271e-97       -0.0\n",
            "D2   24     3.016311e-116        0.0\n",
            "     48     2.756767e-138        0.0\n",
            "     72     -0.000000e+00        0.0\n",
            "\n",
            " mean\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24        -0.251760   0.367996\n",
            "     48        -0.122773   0.412469\n",
            "     72        -0.295271   0.286250\n",
            "D2   24         0.010754   0.115253\n",
            "     48         0.013174   0.214480\n",
            "     72         0.070250   0.253439\n",
            "\n",
            " std\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24         0.751395   1.461954\n",
            "     48         0.853396   1.693903\n",
            "     72         0.769294   1.458600\n",
            "D2   24         0.836626   1.044999\n",
            "     48         0.836845   1.315476\n",
            "     72         0.878041   1.451532\n",
            "\n",
            " var\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24         0.564594   2.137311\n",
            "     48         0.728284   2.869308\n",
            "     72         0.591814   2.127514\n",
            "D2   24         0.699943   1.092022\n",
            "     48         0.700310   1.730477\n",
            "     72         0.770956   2.106944\n",
            "\n",
            " min\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24           -2.370     -5.183\n",
            "     48           -3.276     -5.420\n",
            "     72           -2.086     -5.261\n",
            "D2   24           -2.135     -5.233\n",
            "     48           -4.859     -4.567\n",
            "     72           -1.909     -5.513\n",
            "\n",
            " max\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24            3.226      9.908\n",
            "     48            3.423     10.000\n",
            "     72            2.584      9.355\n",
            "D2   24            2.601      7.167\n",
            "     48            4.335     10.000\n",
            "     72            4.670     10.000\n",
            "\n",
            " median\n",
            "tratamento  com_controle  com_droga\n",
            "dose tempo                         \n",
            "D1   24          -0.2912    0.02700\n",
            "     48          -0.2145    0.01620\n",
            "     72          -0.3655    0.02615\n",
            "D2   24           0.0096   -0.02610\n",
            "     48          -0.0506   -0.02960\n",
            "     72           0.0528    0.00015\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ucEr5k6Lg8c"
      },
      "source": [
        "Peguei a resposta dessa exploração em loop do notebook [LucasDatilioCarderelli](https://github.com/LucasDatilioCarderelli/ImersaoDados3/blob/main/Aula_03.ipynb) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I05zbeJ-a4N7"
      },
      "source": [
        "### Desafio 04\n",
        "Explorar o melt."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avhYxya9uM4Z"
      },
      "source": [
        "O pd.melt() \"derrete\" as colunas \"value_vars. Esta função é útil para massagear um DataFrame em um formato onde uma ou mais colunas são variáveis identificadoras (id_vars), enquanto todas as outras colunas, consideradas variáveis medidas (value_vars), são \"não dinamizadas\" para o eixo da linha, deixando apenas dois não identificadores colunas, 'variável' e 'valor'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "ncucmToXn0bg",
        "outputId": "8148e092-df1a-4657-ecc6-538fb513d950"
      },
      "source": [
        "columns=dados.loc[:, 'g-0': 'g-10'].columns\n",
        "pd.melt(dados, id_vars=['dose', 'tempo', 'tratamento'], value_vars=columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dose</th>\n",
              "      <th>tempo</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>D1</td>\n",
              "      <td>24</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-0</td>\n",
              "      <td>1.0620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>D1</td>\n",
              "      <td>72</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-0</td>\n",
              "      <td>0.0743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>D1</td>\n",
              "      <td>48</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-0</td>\n",
              "      <td>0.6280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>D1</td>\n",
              "      <td>48</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-0</td>\n",
              "      <td>-0.5138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>D2</td>\n",
              "      <td>72</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-0</td>\n",
              "      <td>-0.3254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261949</th>\n",
              "      <td>D2</td>\n",
              "      <td>24</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-10</td>\n",
              "      <td>0.2531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261950</th>\n",
              "      <td>D2</td>\n",
              "      <td>24</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-10</td>\n",
              "      <td>-0.1851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261951</th>\n",
              "      <td>D2</td>\n",
              "      <td>48</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>g-10</td>\n",
              "      <td>-0.1854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261952</th>\n",
              "      <td>D1</td>\n",
              "      <td>24</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-10</td>\n",
              "      <td>0.5742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261953</th>\n",
              "      <td>D1</td>\n",
              "      <td>72</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>g-10</td>\n",
              "      <td>1.9460</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>261954 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       dose  tempo    tratamento variable   value\n",
              "0        D1     24     com_droga      g-0  1.0620\n",
              "1        D1     72     com_droga      g-0  0.0743\n",
              "2        D1     48     com_droga      g-0  0.6280\n",
              "3        D1     48     com_droga      g-0 -0.5138\n",
              "4        D2     72     com_droga      g-0 -0.3254\n",
              "...     ...    ...           ...      ...     ...\n",
              "261949   D2     24     com_droga     g-10  0.2531\n",
              "261950   D2     24     com_droga     g-10 -0.1851\n",
              "261951   D2     48  com_controle     g-10 -0.1854\n",
              "261952   D1     24     com_droga     g-10  0.5742\n",
              "261953   D1     72     com_droga     g-10  1.9460\n",
              "\n",
              "[261954 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "fg-yIk5RyQpH",
        "outputId": "4e3256a5-ea2d-4a35-bf7c-b27e96992e56"
      },
      "source": [
        "pd.melt(dados, id_vars='g-0', value_vars='composto').sort_values(by='g-0')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>g-0</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>-5.513</td>\n",
              "      <td>composto</td>\n",
              "      <td>d1d4140f4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7834</th>\n",
              "      <td>-5.420</td>\n",
              "      <td>composto</td>\n",
              "      <td>82a5720b8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>-5.261</td>\n",
              "      <td>composto</td>\n",
              "      <td>2002e6125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5117</th>\n",
              "      <td>-5.233</td>\n",
              "      <td>composto</td>\n",
              "      <td>b4c9484ae</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19974</th>\n",
              "      <td>-5.183</td>\n",
              "      <td>composto</td>\n",
              "      <td>53d56037b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18144</th>\n",
              "      <td>10.000</td>\n",
              "      <td>composto</td>\n",
              "      <td>87d714366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19405</th>\n",
              "      <td>10.000</td>\n",
              "      <td>composto</td>\n",
              "      <td>87d714366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>10.000</td>\n",
              "      <td>composto</td>\n",
              "      <td>87d714366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4051</th>\n",
              "      <td>10.000</td>\n",
              "      <td>composto</td>\n",
              "      <td>87d714366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2428</th>\n",
              "      <td>10.000</td>\n",
              "      <td>composto</td>\n",
              "      <td>87d714366</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-0  variable      value\n",
              "726    -5.513  composto  d1d4140f4\n",
              "7834   -5.420  composto  82a5720b8\n",
              "1128   -5.261  composto  2002e6125\n",
              "5117   -5.233  composto  b4c9484ae\n",
              "19974  -5.183  composto  53d56037b\n",
              "...       ...       ...        ...\n",
              "18144  10.000  composto  87d714366\n",
              "19405  10.000  composto  87d714366\n",
              "119    10.000  composto  87d714366\n",
              "4051   10.000  composto  87d714366\n",
              "2428   10.000  composto  87d714366\n",
              "\n",
              "[23814 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7HfjsZfzT7R"
      },
      "source": [
        "\n",
        "### Desafio 05\n",
        "Correlacionar as células com os genes. Refletir sobre os efeitos biológicos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "2wyVp1yT77Sp",
        "outputId": "79046f94-88fc-4418-d4e5-170a913bfce0"
      },
      "source": [
        "## Correlação de dados do g0 ao g99 com o conjunto de c\n",
        "gc_corr = dados.loc[:, 'g-0':'g-99'].merge(dados.loc[:, 'c-0':'c-99'], on=dados['id']).corr()\n",
        "gc_corr = gc_corr.iloc[:100, 100:] ## seleção por posição para correlacionar o g ao c\n",
        "gc_corr"
      ],
      "execution_count": null,
      "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",
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              "    }\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>c-0</th>\n",
              "      <th>c-1</th>\n",
              "      <th>c-2</th>\n",
              "      <th>c-3</th>\n",
              "      <th>c-4</th>\n",
              "      <th>c-5</th>\n",
              "      <th>c-6</th>\n",
              "      <th>c-7</th>\n",
              "      <th>c-8</th>\n",
              "      <th>c-9</th>\n",
              "      <th>c-10</th>\n",
              "      <th>c-11</th>\n",
              "      <th>c-12</th>\n",
              "      <th>c-13</th>\n",
              "      <th>c-14</th>\n",
              "      <th>c-15</th>\n",
              "      <th>c-16</th>\n",
              "      <th>c-17</th>\n",
              "      <th>c-18</th>\n",
              "      <th>c-19</th>\n",
              "      <th>c-20</th>\n",
              "      <th>c-21</th>\n",
              "      <th>c-22</th>\n",
              "      <th>c-23</th>\n",
              "      <th>c-24</th>\n",
              "      <th>c-25</th>\n",
              "      <th>c-26</th>\n",
              "      <th>c-27</th>\n",
              "      <th>c-28</th>\n",
              "      <th>c-29</th>\n",
              "      <th>c-30</th>\n",
              "      <th>c-31</th>\n",
              "      <th>c-32</th>\n",
              "      <th>c-33</th>\n",
              "      <th>c-34</th>\n",
              "      <th>c-35</th>\n",
              "      <th>c-36</th>\n",
              "      <th>c-37</th>\n",
              "      <th>c-38</th>\n",
              "      <th>c-39</th>\n",
              "      <th>...</th>\n",
              "      <th>c-60</th>\n",
              "      <th>c-61</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g-0</th>\n",
              "      <td>-0.624116</td>\n",
              "      <td>-0.624810</td>\n",
              "      <td>-0.639789</td>\n",
              "      <td>-0.604041</td>\n",
              "      <td>-0.606231</td>\n",
              "      <td>-0.629189</td>\n",
              "      <td>-0.669697</td>\n",
              "      <td>-0.608954</td>\n",
              "      <td>-0.642211</td>\n",
              "      <td>-0.630989</td>\n",
              "      <td>-0.626020</td>\n",
              "      <td>-0.631486</td>\n",
              "      <td>-0.629841</td>\n",
              "      <td>-0.643529</td>\n",
              "      <td>-0.622502</td>\n",
              "      <td>-0.647751</td>\n",
              "      <td>-0.638829</td>\n",
              "      <td>-0.678957</td>\n",
              "      <td>-0.594877</td>\n",
              "      <td>-0.643239</td>\n",
              "      <td>-0.684132</td>\n",
              "      <td>-0.630950</td>\n",
              "      <td>-0.543857</td>\n",
              "      <td>-0.579103</td>\n",
              "      <td>-0.660192</td>\n",
              "      <td>-0.605740</td>\n",
              "      <td>-0.620731</td>\n",
              "      <td>-0.661781</td>\n",
              "      <td>-0.647510</td>\n",
              "      <td>-0.615229</td>\n",
              "      <td>-0.653816</td>\n",
              "      <td>-0.659935</td>\n",
              "      <td>-0.646008</td>\n",
              "      <td>-0.656088</td>\n",
              "      <td>-0.649368</td>\n",
              "      <td>-0.639943</td>\n",
              "      <td>-0.606850</td>\n",
              "      <td>-0.565831</td>\n",
              "      <td>-0.627974</td>\n",
              "      <td>-0.645185</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.662186</td>\n",
              "      <td>-0.639153</td>\n",
              "      <td>-0.665249</td>\n",
              "      <td>-0.604982</td>\n",
              "      <td>-0.621192</td>\n",
              "      <td>-0.646914</td>\n",
              "      <td>-0.630096</td>\n",
              "      <td>-0.625521</td>\n",
              "      <td>-0.643556</td>\n",
              "      <td>-0.622139</td>\n",
              "      <td>-0.655613</td>\n",
              "      <td>-0.637361</td>\n",
              "      <td>-0.669361</td>\n",
              "      <td>-0.666255</td>\n",
              "      <td>-0.541994</td>\n",
              "      <td>-0.660842</td>\n",
              "      <td>-0.540702</td>\n",
              "      <td>-0.647280</td>\n",
              "      <td>-0.670072</td>\n",
              "      <td>-0.592806</td>\n",
              "      <td>-0.644508</td>\n",
              "      <td>-0.577032</td>\n",
              "      <td>-0.621314</td>\n",
              "      <td>-0.673295</td>\n",
              "      <td>-0.588529</td>\n",
              "      <td>-0.660818</td>\n",
              "      <td>-0.625025</td>\n",
              "      <td>-0.625130</td>\n",
              "      <td>-0.627185</td>\n",
              "      <td>-0.682893</td>\n",
              "      <td>-0.648726</td>\n",
              "      <td>-0.629025</td>\n",
              "      <td>-0.633479</td>\n",
              "      <td>-0.668520</td>\n",
              "      <td>-0.656116</td>\n",
              "      <td>-0.600758</td>\n",
              "      <td>-0.646784</td>\n",
              "      <td>-0.622840</td>\n",
              "      <td>-0.640484</td>\n",
              "      <td>-0.609238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-1</th>\n",
              "      <td>-0.037405</td>\n",
              "      <td>-0.030558</td>\n",
              "      <td>-0.039616</td>\n",
              "      <td>0.011899</td>\n",
              "      <td>-0.027014</td>\n",
              "      <td>-0.004664</td>\n",
              "      <td>-0.027941</td>\n",
              "      <td>-0.003155</td>\n",
              "      <td>-0.016199</td>\n",
              "      <td>0.012004</td>\n",
              "      <td>-0.011896</td>\n",
              "      <td>-0.028692</td>\n",
              "      <td>-0.016804</td>\n",
              "      <td>-0.039030</td>\n",
              "      <td>-0.018724</td>\n",
              "      <td>0.001283</td>\n",
              "      <td>-0.020506</td>\n",
              "      <td>-0.028191</td>\n",
              "      <td>-0.006079</td>\n",
              "      <td>-0.004661</td>\n",
              "      <td>-0.009059</td>\n",
              "      <td>-0.013806</td>\n",
              "      <td>0.052851</td>\n",
              "      <td>0.019902</td>\n",
              "      <td>-0.015551</td>\n",
              "      <td>0.020494</td>\n",
              "      <td>-0.021502</td>\n",
              "      <td>-0.023190</td>\n",
              "      <td>-0.026067</td>\n",
              "      <td>-0.016824</td>\n",
              "      <td>-0.008202</td>\n",
              "      <td>-0.035886</td>\n",
              "      <td>-0.020831</td>\n",
              "      <td>-0.016770</td>\n",
              "      <td>0.006563</td>\n",
              "      <td>-0.005731</td>\n",
              "      <td>-0.032461</td>\n",
              "      <td>-0.037041</td>\n",
              "      <td>-0.027810</td>\n",
              "      <td>0.001859</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.040883</td>\n",
              "      <td>-0.027389</td>\n",
              "      <td>-0.013518</td>\n",
              "      <td>-0.005513</td>\n",
              "      <td>0.004970</td>\n",
              "      <td>0.000605</td>\n",
              "      <td>0.005983</td>\n",
              "      <td>-0.025452</td>\n",
              "      <td>-0.014658</td>\n",
              "      <td>-0.016301</td>\n",
              "      <td>-0.035473</td>\n",
              "      <td>0.004021</td>\n",
              "      <td>-0.041172</td>\n",
              "      <td>-0.023066</td>\n",
              "      <td>0.004179</td>\n",
              "      <td>-0.020136</td>\n",
              "      <td>0.023721</td>\n",
              "      <td>-0.003363</td>\n",
              "      <td>-0.044079</td>\n",
              "      <td>0.006385</td>\n",
              "      <td>-0.004805</td>\n",
              "      <td>-0.010185</td>\n",
              "      <td>0.002633</td>\n",
              "      <td>-0.022321</td>\n",
              "      <td>-0.018388</td>\n",
              "      <td>-0.020843</td>\n",
              "      <td>-0.016555</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>-0.037424</td>\n",
              "      <td>-0.008322</td>\n",
              "      <td>-0.034557</td>\n",
              "      <td>-0.000934</td>\n",
              "      <td>-0.022478</td>\n",
              "      <td>-0.034891</td>\n",
              "      <td>-0.042638</td>\n",
              "      <td>-0.011237</td>\n",
              "      <td>-0.022127</td>\n",
              "      <td>-0.008487</td>\n",
              "      <td>-0.002824</td>\n",
              "      <td>-0.002308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-2</th>\n",
              "      <td>-0.168656</td>\n",
              "      <td>-0.178692</td>\n",
              "      <td>-0.176733</td>\n",
              "      <td>-0.156894</td>\n",
              "      <td>-0.180127</td>\n",
              "      <td>-0.170701</td>\n",
              "      <td>-0.181008</td>\n",
              "      <td>-0.175473</td>\n",
              "      <td>-0.178634</td>\n",
              "      <td>-0.163261</td>\n",
              "      <td>-0.180958</td>\n",
              "      <td>-0.176356</td>\n",
              "      <td>-0.180042</td>\n",
              "      <td>-0.183370</td>\n",
              "      <td>-0.174772</td>\n",
              "      <td>-0.180545</td>\n",
              "      <td>-0.162327</td>\n",
              "      <td>-0.177785</td>\n",
              "      <td>-0.169176</td>\n",
              "      <td>-0.170238</td>\n",
              "      <td>-0.182883</td>\n",
              "      <td>-0.172854</td>\n",
              "      <td>-0.137744</td>\n",
              "      <td>-0.193688</td>\n",
              "      <td>-0.170649</td>\n",
              "      <td>-0.171545</td>\n",
              "      <td>-0.179031</td>\n",
              "      <td>-0.184632</td>\n",
              "      <td>-0.162529</td>\n",
              "      <td>-0.166055</td>\n",
              "      <td>-0.177225</td>\n",
              "      <td>-0.185423</td>\n",
              "      <td>-0.184273</td>\n",
              "      <td>-0.187304</td>\n",
              "      <td>-0.173426</td>\n",
              "      <td>-0.187504</td>\n",
              "      <td>-0.160625</td>\n",
              "      <td>-0.160252</td>\n",
              "      <td>-0.181268</td>\n",
              "      <td>-0.183121</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.179885</td>\n",
              "      <td>-0.183319</td>\n",
              "      <td>-0.185323</td>\n",
              "      <td>-0.174439</td>\n",
              "      <td>-0.163617</td>\n",
              "      <td>-0.174381</td>\n",
              "      <td>-0.185913</td>\n",
              "      <td>-0.166461</td>\n",
              "      <td>-0.181092</td>\n",
              "      <td>-0.167242</td>\n",
              "      <td>-0.180925</td>\n",
              "      <td>-0.178400</td>\n",
              "      <td>-0.175939</td>\n",
              "      <td>-0.182079</td>\n",
              "      <td>-0.151960</td>\n",
              "      <td>-0.183145</td>\n",
              "      <td>-0.181286</td>\n",
              "      <td>-0.159720</td>\n",
              "      <td>-0.179919</td>\n",
              "      <td>-0.162542</td>\n",
              "      <td>-0.183613</td>\n",
              "      <td>-0.170641</td>\n",
              "      <td>-0.186154</td>\n",
              "      <td>-0.177631</td>\n",
              "      <td>-0.166103</td>\n",
              "      <td>-0.187158</td>\n",
              "      <td>-0.157164</td>\n",
              "      <td>-0.170382</td>\n",
              "      <td>-0.164975</td>\n",
              "      <td>-0.174041</td>\n",
              "      <td>-0.183325</td>\n",
              "      <td>-0.186048</td>\n",
              "      <td>-0.166786</td>\n",
              "      <td>-0.190383</td>\n",
              "      <td>-0.178312</td>\n",
              "      <td>-0.158105</td>\n",
              "      <td>-0.182540</td>\n",
              "      <td>-0.178812</td>\n",
              "      <td>-0.170152</td>\n",
              "      <td>-0.172124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-3</th>\n",
              "      <td>-0.037342</td>\n",
              "      <td>-0.093505</td>\n",
              "      <td>-0.085298</td>\n",
              "      <td>-0.029079</td>\n",
              "      <td>-0.065964</td>\n",
              "      <td>-0.054947</td>\n",
              "      <td>-0.118651</td>\n",
              "      <td>-0.031232</td>\n",
              "      <td>-0.060544</td>\n",
              "      <td>-0.098435</td>\n",
              "      <td>-0.056865</td>\n",
              "      <td>-0.072926</td>\n",
              "      <td>-0.081498</td>\n",
              "      <td>-0.078792</td>\n",
              "      <td>-0.072001</td>\n",
              "      <td>-0.041614</td>\n",
              "      <td>-0.057309</td>\n",
              "      <td>-0.085462</td>\n",
              "      <td>-0.064829</td>\n",
              "      <td>-0.025428</td>\n",
              "      <td>-0.069663</td>\n",
              "      <td>-0.070663</td>\n",
              "      <td>0.070106</td>\n",
              "      <td>-0.001622</td>\n",
              "      <td>-0.060156</td>\n",
              "      <td>-0.007329</td>\n",
              "      <td>-0.108669</td>\n",
              "      <td>-0.068180</td>\n",
              "      <td>-0.084874</td>\n",
              "      <td>-0.038126</td>\n",
              "      <td>-0.057558</td>\n",
              "      <td>-0.096243</td>\n",
              "      <td>-0.040765</td>\n",
              "      <td>-0.091024</td>\n",
              "      <td>-0.042440</td>\n",
              "      <td>-0.048595</td>\n",
              "      <td>-0.077177</td>\n",
              "      <td>-0.033611</td>\n",
              "      <td>-0.101452</td>\n",
              "      <td>-0.028037</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.115228</td>\n",
              "      <td>-0.037189</td>\n",
              "      <td>-0.077496</td>\n",
              "      <td>-0.060176</td>\n",
              "      <td>-0.026180</td>\n",
              "      <td>-0.099132</td>\n",
              "      <td>-0.026693</td>\n",
              "      <td>-0.060265</td>\n",
              "      <td>-0.036769</td>\n",
              "      <td>-0.045193</td>\n",
              "      <td>-0.140946</td>\n",
              "      <td>-0.026295</td>\n",
              "      <td>-0.076285</td>\n",
              "      <td>-0.055792</td>\n",
              "      <td>-0.035812</td>\n",
              "      <td>-0.078715</td>\n",
              "      <td>-0.050296</td>\n",
              "      <td>-0.071568</td>\n",
              "      <td>-0.078478</td>\n",
              "      <td>-0.046407</td>\n",
              "      <td>-0.033641</td>\n",
              "      <td>-0.064704</td>\n",
              "      <td>-0.044155</td>\n",
              "      <td>-0.083462</td>\n",
              "      <td>-0.028035</td>\n",
              "      <td>-0.063797</td>\n",
              "      <td>-0.039701</td>\n",
              "      <td>0.010285</td>\n",
              "      <td>-0.047301</td>\n",
              "      <td>-0.066781</td>\n",
              "      <td>-0.091892</td>\n",
              "      <td>-0.045385</td>\n",
              "      <td>-0.089801</td>\n",
              "      <td>-0.089818</td>\n",
              "      <td>-0.081073</td>\n",
              "      <td>-0.016778</td>\n",
              "      <td>-0.083846</td>\n",
              "      <td>-0.049951</td>\n",
              "      <td>-0.079855</td>\n",
              "      <td>-0.037579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-4</th>\n",
              "      <td>-0.403545</td>\n",
              "      <td>-0.379840</td>\n",
              "      <td>-0.392841</td>\n",
              "      <td>-0.357850</td>\n",
              "      <td>-0.365118</td>\n",
              "      <td>-0.384914</td>\n",
              "      <td>-0.364633</td>\n",
              "      <td>-0.378776</td>\n",
              "      <td>-0.383263</td>\n",
              "      <td>-0.346834</td>\n",
              "      <td>-0.379019</td>\n",
              "      <td>-0.384599</td>\n",
              "      <td>-0.379915</td>\n",
              "      <td>-0.389980</td>\n",
              "      <td>-0.395484</td>\n",
              "      <td>-0.382739</td>\n",
              "      <td>-0.387586</td>\n",
              "      <td>-0.390418</td>\n",
              "      <td>-0.342299</td>\n",
              "      <td>-0.394375</td>\n",
              "      <td>-0.404137</td>\n",
              "      <td>-0.378579</td>\n",
              "      <td>-0.319028</td>\n",
              "      <td>-0.351363</td>\n",
              "      <td>-0.393502</td>\n",
              "      <td>-0.372542</td>\n",
              "      <td>-0.357408</td>\n",
              "      <td>-0.406761</td>\n",
              "      <td>-0.382014</td>\n",
              "      <td>-0.381405</td>\n",
              "      <td>-0.376348</td>\n",
              "      <td>-0.390938</td>\n",
              "      <td>-0.415720</td>\n",
              "      <td>-0.369416</td>\n",
              "      <td>-0.393812</td>\n",
              "      <td>-0.380305</td>\n",
              "      <td>-0.385241</td>\n",
              "      <td>-0.360443</td>\n",
              "      <td>-0.365057</td>\n",
              "      <td>-0.386042</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.399687</td>\n",
              "      <td>-0.402933</td>\n",
              "      <td>-0.360525</td>\n",
              "      <td>-0.335792</td>\n",
              "      <td>-0.392328</td>\n",
              "      <td>-0.365328</td>\n",
              "      <td>-0.358876</td>\n",
              "      <td>-0.379927</td>\n",
              "      <td>-0.393416</td>\n",
              "      <td>-0.380265</td>\n",
              "      <td>-0.378893</td>\n",
              "      <td>-0.396324</td>\n",
              "      <td>-0.407893</td>\n",
              "      <td>-0.396741</td>\n",
              "      <td>-0.333530</td>\n",
              "      <td>-0.397591</td>\n",
              "      <td>-0.302705</td>\n",
              "      <td>-0.379939</td>\n",
              "      <td>-0.405154</td>\n",
              "      <td>-0.361345</td>\n",
              "      <td>-0.378939</td>\n",
              "      <td>-0.361197</td>\n",
              "      <td>-0.351890</td>\n",
              "      <td>-0.398071</td>\n",
              "      <td>-0.379515</td>\n",
              "      <td>-0.399595</td>\n",
              "      <td>-0.389186</td>\n",
              "      <td>-0.399976</td>\n",
              "      <td>-0.406509</td>\n",
              "      <td>-0.413806</td>\n",
              "      <td>-0.387193</td>\n",
              "      <td>-0.361679</td>\n",
              "      <td>-0.351508</td>\n",
              "      <td>-0.397062</td>\n",
              "      <td>-0.388885</td>\n",
              "      <td>-0.378084</td>\n",
              "      <td>-0.379085</td>\n",
              "      <td>-0.394904</td>\n",
              "      <td>-0.366693</td>\n",
              "      <td>-0.390581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-95</th>\n",
              "      <td>-0.636594</td>\n",
              "      <td>-0.695183</td>\n",
              "      <td>-0.700688</td>\n",
              "      <td>-0.617771</td>\n",
              "      <td>-0.690192</td>\n",
              "      <td>-0.680735</td>\n",
              "      <td>-0.707364</td>\n",
              "      <td>-0.614260</td>\n",
              "      <td>-0.687668</td>\n",
              "      <td>-0.671382</td>\n",
              "      <td>-0.690634</td>\n",
              "      <td>-0.697639</td>\n",
              "      <td>-0.693942</td>\n",
              "      <td>-0.705645</td>\n",
              "      <td>-0.653173</td>\n",
              "      <td>-0.678601</td>\n",
              "      <td>-0.611411</td>\n",
              "      <td>-0.694287</td>\n",
              "      <td>-0.683816</td>\n",
              "      <td>-0.629035</td>\n",
              "      <td>-0.683080</td>\n",
              "      <td>-0.685627</td>\n",
              "      <td>-0.539317</td>\n",
              "      <td>-0.651757</td>\n",
              "      <td>-0.680245</td>\n",
              "      <td>-0.656982</td>\n",
              "      <td>-0.707109</td>\n",
              "      <td>-0.681596</td>\n",
              "      <td>-0.686001</td>\n",
              "      <td>-0.611538</td>\n",
              "      <td>-0.664305</td>\n",
              "      <td>-0.690436</td>\n",
              "      <td>-0.649974</td>\n",
              "      <td>-0.706555</td>\n",
              "      <td>-0.680135</td>\n",
              "      <td>-0.653593</td>\n",
              "      <td>-0.678990</td>\n",
              "      <td>-0.572977</td>\n",
              "      <td>-0.709169</td>\n",
              "      <td>-0.657275</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.701153</td>\n",
              "      <td>-0.629761</td>\n",
              "      <td>-0.689786</td>\n",
              "      <td>-0.694755</td>\n",
              "      <td>-0.640799</td>\n",
              "      <td>-0.693734</td>\n",
              "      <td>-0.680991</td>\n",
              "      <td>-0.677442</td>\n",
              "      <td>-0.664269</td>\n",
              "      <td>-0.633418</td>\n",
              "      <td>-0.695219</td>\n",
              "      <td>-0.639614</td>\n",
              "      <td>-0.689950</td>\n",
              "      <td>-0.697076</td>\n",
              "      <td>-0.569446</td>\n",
              "      <td>-0.695588</td>\n",
              "      <td>-0.665751</td>\n",
              "      <td>-0.664253</td>\n",
              "      <td>-0.675154</td>\n",
              "      <td>-0.654573</td>\n",
              "      <td>-0.671785</td>\n",
              "      <td>-0.686341</td>\n",
              "      <td>-0.683424</td>\n",
              "      <td>-0.684212</td>\n",
              "      <td>-0.674541</td>\n",
              "      <td>-0.683444</td>\n",
              "      <td>-0.636855</td>\n",
              "      <td>-0.609868</td>\n",
              "      <td>-0.642493</td>\n",
              "      <td>-0.654921</td>\n",
              "      <td>-0.692247</td>\n",
              "      <td>-0.675949</td>\n",
              "      <td>-0.677949</td>\n",
              "      <td>-0.696292</td>\n",
              "      <td>-0.704551</td>\n",
              "      <td>-0.622818</td>\n",
              "      <td>-0.695815</td>\n",
              "      <td>-0.671934</td>\n",
              "      <td>-0.652625</td>\n",
              "      <td>-0.619838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-96</th>\n",
              "      <td>0.531682</td>\n",
              "      <td>0.617674</td>\n",
              "      <td>0.616228</td>\n",
              "      <td>0.538991</td>\n",
              "      <td>0.589075</td>\n",
              "      <td>0.580979</td>\n",
              "      <td>0.651348</td>\n",
              "      <td>0.530023</td>\n",
              "      <td>0.604272</td>\n",
              "      <td>0.634923</td>\n",
              "      <td>0.605798</td>\n",
              "      <td>0.615674</td>\n",
              "      <td>0.640745</td>\n",
              "      <td>0.608154</td>\n",
              "      <td>0.576760</td>\n",
              "      <td>0.601126</td>\n",
              "      <td>0.554610</td>\n",
              "      <td>0.636374</td>\n",
              "      <td>0.591549</td>\n",
              "      <td>0.542093</td>\n",
              "      <td>0.637725</td>\n",
              "      <td>0.589712</td>\n",
              "      <td>0.429178</td>\n",
              "      <td>0.554203</td>\n",
              "      <td>0.597277</td>\n",
              "      <td>0.535860</td>\n",
              "      <td>0.638959</td>\n",
              "      <td>0.608763</td>\n",
              "      <td>0.616642</td>\n",
              "      <td>0.528944</td>\n",
              "      <td>0.592251</td>\n",
              "      <td>0.636219</td>\n",
              "      <td>0.567169</td>\n",
              "      <td>0.651412</td>\n",
              "      <td>0.601177</td>\n",
              "      <td>0.592107</td>\n",
              "      <td>0.571842</td>\n",
              "      <td>0.483110</td>\n",
              "      <td>0.649469</td>\n",
              "      <td>0.568629</td>\n",
              "      <td>...</td>\n",
              "      <td>0.646864</td>\n",
              "      <td>0.544066</td>\n",
              "      <td>0.615705</td>\n",
              "      <td>0.624645</td>\n",
              "      <td>0.529006</td>\n",
              "      <td>0.634646</td>\n",
              "      <td>0.581391</td>\n",
              "      <td>0.602749</td>\n",
              "      <td>0.591472</td>\n",
              "      <td>0.550910</td>\n",
              "      <td>0.665125</td>\n",
              "      <td>0.554225</td>\n",
              "      <td>0.617730</td>\n",
              "      <td>0.591513</td>\n",
              "      <td>0.491442</td>\n",
              "      <td>0.625034</td>\n",
              "      <td>0.591556</td>\n",
              "      <td>0.624058</td>\n",
              "      <td>0.616246</td>\n",
              "      <td>0.554583</td>\n",
              "      <td>0.590542</td>\n",
              "      <td>0.579527</td>\n",
              "      <td>0.596446</td>\n",
              "      <td>0.615811</td>\n",
              "      <td>0.549177</td>\n",
              "      <td>0.615389</td>\n",
              "      <td>0.540844</td>\n",
              "      <td>0.525381</td>\n",
              "      <td>0.551646</td>\n",
              "      <td>0.600294</td>\n",
              "      <td>0.618503</td>\n",
              "      <td>0.615484</td>\n",
              "      <td>0.604430</td>\n",
              "      <td>0.639115</td>\n",
              "      <td>0.614968</td>\n",
              "      <td>0.511366</td>\n",
              "      <td>0.613003</td>\n",
              "      <td>0.591373</td>\n",
              "      <td>0.577432</td>\n",
              "      <td>0.533321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-97</th>\n",
              "      <td>-0.669864</td>\n",
              "      <td>-0.652777</td>\n",
              "      <td>-0.665102</td>\n",
              "      <td>-0.634263</td>\n",
              "      <td>-0.648563</td>\n",
              "      <td>-0.663034</td>\n",
              "      <td>-0.660039</td>\n",
              "      <td>-0.631289</td>\n",
              "      <td>-0.670035</td>\n",
              "      <td>-0.621106</td>\n",
              "      <td>-0.653558</td>\n",
              "      <td>-0.658624</td>\n",
              "      <td>-0.656332</td>\n",
              "      <td>-0.645153</td>\n",
              "      <td>-0.650085</td>\n",
              "      <td>-0.658315</td>\n",
              "      <td>-0.643527</td>\n",
              "      <td>-0.667195</td>\n",
              "      <td>-0.612355</td>\n",
              "      <td>-0.639702</td>\n",
              "      <td>-0.680811</td>\n",
              "      <td>-0.646201</td>\n",
              "      <td>-0.536483</td>\n",
              "      <td>-0.618567</td>\n",
              "      <td>-0.643434</td>\n",
              "      <td>-0.642538</td>\n",
              "      <td>-0.636548</td>\n",
              "      <td>-0.668354</td>\n",
              "      <td>-0.651386</td>\n",
              "      <td>-0.625685</td>\n",
              "      <td>-0.657886</td>\n",
              "      <td>-0.680991</td>\n",
              "      <td>-0.666105</td>\n",
              "      <td>-0.652845</td>\n",
              "      <td>-0.662838</td>\n",
              "      <td>-0.666904</td>\n",
              "      <td>-0.650919</td>\n",
              "      <td>-0.580481</td>\n",
              "      <td>-0.637141</td>\n",
              "      <td>-0.678364</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.654467</td>\n",
              "      <td>-0.673243</td>\n",
              "      <td>-0.673745</td>\n",
              "      <td>-0.635252</td>\n",
              "      <td>-0.633273</td>\n",
              "      <td>-0.646733</td>\n",
              "      <td>-0.663780</td>\n",
              "      <td>-0.648898</td>\n",
              "      <td>-0.664788</td>\n",
              "      <td>-0.636805</td>\n",
              "      <td>-0.657974</td>\n",
              "      <td>-0.664927</td>\n",
              "      <td>-0.671300</td>\n",
              "      <td>-0.648724</td>\n",
              "      <td>-0.582530</td>\n",
              "      <td>-0.664253</td>\n",
              "      <td>-0.575880</td>\n",
              "      <td>-0.634755</td>\n",
              "      <td>-0.668918</td>\n",
              "      <td>-0.630040</td>\n",
              "      <td>-0.659518</td>\n",
              "      <td>-0.624910</td>\n",
              "      <td>-0.658097</td>\n",
              "      <td>-0.682234</td>\n",
              "      <td>-0.640194</td>\n",
              "      <td>-0.677215</td>\n",
              "      <td>-0.620579</td>\n",
              "      <td>-0.656658</td>\n",
              "      <td>-0.671416</td>\n",
              "      <td>-0.681505</td>\n",
              "      <td>-0.665017</td>\n",
              "      <td>-0.661652</td>\n",
              "      <td>-0.642323</td>\n",
              "      <td>-0.658989</td>\n",
              "      <td>-0.656833</td>\n",
              "      <td>-0.635910</td>\n",
              "      <td>-0.665809</td>\n",
              "      <td>-0.648235</td>\n",
              "      <td>-0.628130</td>\n",
              "      <td>-0.603356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-98</th>\n",
              "      <td>0.551045</td>\n",
              "      <td>0.564356</td>\n",
              "      <td>0.578369</td>\n",
              "      <td>0.534653</td>\n",
              "      <td>0.545913</td>\n",
              "      <td>0.579626</td>\n",
              "      <td>0.577630</td>\n",
              "      <td>0.558465</td>\n",
              "      <td>0.583075</td>\n",
              "      <td>0.584127</td>\n",
              "      <td>0.590993</td>\n",
              "      <td>0.566932</td>\n",
              "      <td>0.587233</td>\n",
              "      <td>0.580089</td>\n",
              "      <td>0.561696</td>\n",
              "      <td>0.594466</td>\n",
              "      <td>0.546477</td>\n",
              "      <td>0.585813</td>\n",
              "      <td>0.558430</td>\n",
              "      <td>0.555907</td>\n",
              "      <td>0.609123</td>\n",
              "      <td>0.574902</td>\n",
              "      <td>0.518162</td>\n",
              "      <td>0.538453</td>\n",
              "      <td>0.585490</td>\n",
              "      <td>0.580013</td>\n",
              "      <td>0.585910</td>\n",
              "      <td>0.590568</td>\n",
              "      <td>0.566898</td>\n",
              "      <td>0.533183</td>\n",
              "      <td>0.588008</td>\n",
              "      <td>0.572114</td>\n",
              "      <td>0.572057</td>\n",
              "      <td>0.594499</td>\n",
              "      <td>0.616096</td>\n",
              "      <td>0.544729</td>\n",
              "      <td>0.566591</td>\n",
              "      <td>0.475472</td>\n",
              "      <td>0.566451</td>\n",
              "      <td>0.579415</td>\n",
              "      <td>...</td>\n",
              "      <td>0.585161</td>\n",
              "      <td>0.562030</td>\n",
              "      <td>0.565763</td>\n",
              "      <td>0.553135</td>\n",
              "      <td>0.568722</td>\n",
              "      <td>0.625052</td>\n",
              "      <td>0.579867</td>\n",
              "      <td>0.536116</td>\n",
              "      <td>0.587556</td>\n",
              "      <td>0.554175</td>\n",
              "      <td>0.603804</td>\n",
              "      <td>0.567237</td>\n",
              "      <td>0.579863</td>\n",
              "      <td>0.604631</td>\n",
              "      <td>0.491440</td>\n",
              "      <td>0.581044</td>\n",
              "      <td>0.527596</td>\n",
              "      <td>0.576159</td>\n",
              "      <td>0.565779</td>\n",
              "      <td>0.582557</td>\n",
              "      <td>0.600253</td>\n",
              "      <td>0.550755</td>\n",
              "      <td>0.589871</td>\n",
              "      <td>0.574046</td>\n",
              "      <td>0.558626</td>\n",
              "      <td>0.594159</td>\n",
              "      <td>0.569365</td>\n",
              "      <td>0.562132</td>\n",
              "      <td>0.547023</td>\n",
              "      <td>0.578932</td>\n",
              "      <td>0.557754</td>\n",
              "      <td>0.582041</td>\n",
              "      <td>0.548091</td>\n",
              "      <td>0.582315</td>\n",
              "      <td>0.561534</td>\n",
              "      <td>0.557152</td>\n",
              "      <td>0.577195</td>\n",
              "      <td>0.575795</td>\n",
              "      <td>0.600138</td>\n",
              "      <td>0.550684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g-99</th>\n",
              "      <td>0.175610</td>\n",
              "      <td>0.149433</td>\n",
              "      <td>0.154872</td>\n",
              "      <td>0.119352</td>\n",
              "      <td>0.140497</td>\n",
              "      <td>0.106661</td>\n",
              "      <td>0.174758</td>\n",
              "      <td>0.173283</td>\n",
              "      <td>0.119694</td>\n",
              "      <td>0.094299</td>\n",
              "      <td>0.132801</td>\n",
              "      <td>0.161517</td>\n",
              "      <td>0.154045</td>\n",
              "      <td>0.167331</td>\n",
              "      <td>0.195927</td>\n",
              "      <td>0.145772</td>\n",
              "      <td>0.245772</td>\n",
              "      <td>0.215797</td>\n",
              "      <td>0.057910</td>\n",
              "      <td>0.151457</td>\n",
              "      <td>0.216802</td>\n",
              "      <td>0.148085</td>\n",
              "      <td>-0.055661</td>\n",
              "      <td>0.128522</td>\n",
              "      <td>0.135246</td>\n",
              "      <td>0.082882</td>\n",
              "      <td>0.105306</td>\n",
              "      <td>0.210775</td>\n",
              "      <td>0.146168</td>\n",
              "      <td>0.197059</td>\n",
              "      <td>0.169724</td>\n",
              "      <td>0.186439</td>\n",
              "      <td>0.217897</td>\n",
              "      <td>0.191170</td>\n",
              "      <td>0.115461</td>\n",
              "      <td>0.190337</td>\n",
              "      <td>0.102609</td>\n",
              "      <td>0.192150</td>\n",
              "      <td>0.137720</td>\n",
              "      <td>0.163001</td>\n",
              "      <td>...</td>\n",
              "      <td>0.211314</td>\n",
              "      <td>0.188205</td>\n",
              "      <td>0.161195</td>\n",
              "      <td>0.110305</td>\n",
              "      <td>0.087698</td>\n",
              "      <td>0.115272</td>\n",
              "      <td>0.112735</td>\n",
              "      <td>0.146969</td>\n",
              "      <td>0.169391</td>\n",
              "      <td>0.164981</td>\n",
              "      <td>0.169695</td>\n",
              "      <td>0.179137</td>\n",
              "      <td>0.197944</td>\n",
              "      <td>0.173870</td>\n",
              "      <td>0.117061</td>\n",
              "      <td>0.173209</td>\n",
              "      <td>0.090250</td>\n",
              "      <td>0.143215</td>\n",
              "      <td>0.226481</td>\n",
              "      <td>0.055728</td>\n",
              "      <td>0.136224</td>\n",
              "      <td>0.089567</td>\n",
              "      <td>0.106589</td>\n",
              "      <td>0.206077</td>\n",
              "      <td>0.094261</td>\n",
              "      <td>0.183116</td>\n",
              "      <td>0.125457</td>\n",
              "      <td>0.174072</td>\n",
              "      <td>0.175942</td>\n",
              "      <td>0.235193</td>\n",
              "      <td>0.185746</td>\n",
              "      <td>0.146647</td>\n",
              "      <td>0.134442</td>\n",
              "      <td>0.215929</td>\n",
              "      <td>0.177927</td>\n",
              "      <td>0.121608</td>\n",
              "      <td>0.184096</td>\n",
              "      <td>0.149463</td>\n",
              "      <td>0.091863</td>\n",
              "      <td>0.152651</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           c-0       c-1       c-2  ...      c-97      c-98      c-99\n",
              "g-0  -0.624116 -0.624810 -0.639789  ... -0.622840 -0.640484 -0.609238\n",
              "g-1  -0.037405 -0.030558 -0.039616  ... -0.008487 -0.002824 -0.002308\n",
              "g-2  -0.168656 -0.178692 -0.176733  ... -0.178812 -0.170152 -0.172124\n",
              "g-3  -0.037342 -0.093505 -0.085298  ... -0.049951 -0.079855 -0.037579\n",
              "g-4  -0.403545 -0.379840 -0.392841  ... -0.394904 -0.366693 -0.390581\n",
              "...        ...       ...       ...  ...       ...       ...       ...\n",
              "g-95 -0.636594 -0.695183 -0.700688  ... -0.671934 -0.652625 -0.619838\n",
              "g-96  0.531682  0.617674  0.616228  ...  0.591373  0.577432  0.533321\n",
              "g-97 -0.669864 -0.652777 -0.665102  ... -0.648235 -0.628130 -0.603356\n",
              "g-98  0.551045  0.564356  0.578369  ...  0.575795  0.600138  0.550684\n",
              "g-99  0.175610  0.149433  0.154872  ...  0.149463  0.091863  0.152651\n",
              "\n",
              "[100 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY6WE8qiO-dY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "8dd50d95-9764-4029-ca6c-fc34fa801bb3"
      },
      "source": [
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(gc_corr, cmap=cmap, center=0, square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74dd955750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-fIHayQBqw_"
      },
      "source": [
        "Esse gráfico mostra apenas coencidências de correlação entre alguns genes com a mostras celulares. Ou seja, as células alí contidas possuêm alguma relação com o gene combinado, ou não."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcLcFuzUzW4m"
      },
      "source": [
        "### Desafio 06\n",
        "Estudar o código que plota a matrix de correlaçao (heatmap)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQ0RLwB-Gyk2"
      },
      "source": [
        "Primeira coisa que a documentação informa é que o seaborn faz uso desses recursos `set_theme()`, `diverging_palette()`, `heatmap()` para gerar a matrix de correlação diagonal.\n",
        "\n",
        "[`set_theme()`](https://seaborn.pydata.org/generated/seaborn.set_theme.html#seaborn.set_theme)\n",
        "\n",
        "Esse recurso permite definir vários parâmetros de tema. Cada conjunto de parâmetros pode ser definido direta ou temporariamente. Consulte as funções referenciadas na documentação para obter mais informações.\n",
        "\n",
        "[`diverging_palette()`](https://seaborn.pydata.org/generated/seaborn.diverging_palette.html#seaborn.diverging_palette)\n",
        "\n",
        "Faça uma paleta divergente entre duas cores HUSL. Se você estiver usando o  IPython Notebook, também pode escolher esta paleta interativamente com a função choose_diverging_palette ().\n",
        "\n",
        "[`heatmap()`](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)\n",
        "\n",
        "Plote dados retangulares como uma matriz codificada por cores. Esta é uma função no nível dos eixos e desenhará o mapa de calor para os eixos ativos no momento, se nenhum for fornecido para o argumento do `ax`. Parte deste espaço dos eixos será tomado e usado para traçar um mapa de cores, a menos que cbar seja `False` ou um eixo separado seja fornecido para `cbar_ax`.\n",
        "\n",
        "Vamos ao código:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVjYSV9ozcpJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "bdaccbab-84d2-41fb-e0ce-9c41e60c0d23"
      },
      "source": [
        "from string import ascii_letters\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set_theme(style=\"white\")\n",
        "\n",
        "# Gerador randomico de dataset\n",
        "rs = np.random.RandomState(33)\n",
        "d = pd.DataFrame(data=rs.normal(size=(100, 26)),\n",
        "                 columns=list(ascii_letters[26:]))\n",
        "\n",
        "# Computando a matrix de correlação do dataset d\n",
        "corr = d.corr()\n",
        "\n",
        "# Gerar uma máscara para o triângulo superior\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Configure a dimensão da figura no matplotlib\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Gerar um mapa de cores divergentes personalizado\n",
        "cmap = sns.diverging_palette(150, 275, s=80, l=55, n=9, as_cmap=True)\n",
        "\n",
        "# Desenhe o mapa de calor com a máscara e a proporção correta (aspect ratio)\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74dd1fcd90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIONttTtzdfD"
      },
      "source": [
        "### Desafio 07\n",
        "Resumo do aprendizado com os dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LO8NrIEHNpV2"
      },
      "source": [
        "O principal aprendizado foi sobre como formular correlações usando os recursos do Python através das suas bibliotecas/pacotes.\n",
        "\n",
        "Por meio da exploração das correlações alguns insight sobre drug investigation foram possíveis, como por exemplo a matrix que correlacionou genes com conjunto celulares. Esse mesmo insight restou claro que a investigação será repetitiva e deve ser de um para muito.\n",
        "\n",
        "Nesse sentido, ainda não li nenhuma documentação de como é feita a investigação, mas tenho a percepção que está estudando os efeitos de várias drogas sob um conjunto celular representativo, ao passo que faz uma relação de  causa e efeitos da amostras celular com a resposta no gene."
      ]
    }
  ]
}