From 3bc2daccc4144d5fc8a27f423656d91b7436bf9e Mon Sep 17 00:00:00 2001
From: silva-felipe <74065606+silva-felipe@users.noreply.github.com>
Date: Thu, 6 May 2021 01:02:17 -0300
Subject: [PATCH] Add files via upload

---
 Aula02(desafios)_imersao_dados.ipynb | 1019 ++++++++++++++++++++++++++
 1 file changed, 1019 insertions(+)
 create mode 100644 Aula02(desafios)_imersao_dados.ipynb

diff --git a/Aula02(desafios)_imersao_dados.ipynb b/Aula02(desafios)_imersao_dados.ipynb
new file mode 100644
index 0000000..d7362ea
--- /dev/null
+++ b/Aula02(desafios)_imersao_dados.ipynb
@@ -0,0 +1,1019 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Aula02(desafios)-imersao_dados.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "Bh5Olzc2za8M"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KQ7lyxo8dqvQ"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "sns.set()"
+      ],
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        },
+        "id": "N04lMqrhkEVM",
+        "outputId": "d21a407b-87c8-4855-d952-998fac49847e"
+      },
+      "source": [
+        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
+        "\n",
+        "dados = pd.read_csv(url_dados, compression = 'zip')\n",
+        "dados.head()\n"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>tratamento</th>\n",
+              "      <th>tempo</th>\n",
+              "      <th>dose</th>\n",
+              "      <th>droga</th>\n",
+              "      <th>g-0</th>\n",
+              "      <th>g-1</th>\n",
+              "      <th>g-2</th>\n",
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+              "      <th>g-16</th>\n",
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+              "      <th>g-30</th>\n",
+              "      <th>g-31</th>\n",
+              "      <th>g-32</th>\n",
+              "      <th>g-33</th>\n",
+              "      <th>g-34</th>\n",
+              "      <th>...</th>\n",
+              "      <th>c-60</th>\n",
+              "      <th>c-61</th>\n",
+              "      <th>c-62</th>\n",
+              "      <th>c-63</th>\n",
+              "      <th>c-64</th>\n",
+              "      <th>c-65</th>\n",
+              "      <th>c-66</th>\n",
+              "      <th>c-67</th>\n",
+              "      <th>c-68</th>\n",
+              "      <th>c-69</th>\n",
+              "      <th>c-70</th>\n",
+              "      <th>c-71</th>\n",
+              "      <th>c-72</th>\n",
+              "      <th>c-73</th>\n",
+              "      <th>c-74</th>\n",
+              "      <th>c-75</th>\n",
+              "      <th>c-76</th>\n",
+              "      <th>c-77</th>\n",
+              "      <th>c-78</th>\n",
+              "      <th>c-79</th>\n",
+              "      <th>c-80</th>\n",
+              "      <th>c-81</th>\n",
+              "      <th>c-82</th>\n",
+              "      <th>c-83</th>\n",
+              "      <th>c-84</th>\n",
+              "      <th>c-85</th>\n",
+              "      <th>c-86</th>\n",
+              "      <th>c-87</th>\n",
+              "      <th>c-88</th>\n",
+              "      <th>c-89</th>\n",
+              "      <th>c-90</th>\n",
+              "      <th>c-91</th>\n",
+              "      <th>c-92</th>\n",
+              "      <th>c-93</th>\n",
+              "      <th>c-94</th>\n",
+              "      <th>c-95</th>\n",
+              "      <th>c-96</th>\n",
+              "      <th>c-97</th>\n",
+              "      <th>c-98</th>\n",
+              "      <th>c-99</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>id_000644bb2</td>\n",
+              "      <td>com_droga</td>\n",
+              "      <td>24</td>\n",
+              "      <td>D1</td>\n",
+              "      <td>b68db1d53</td>\n",
+              "      <td>1.0620</td>\n",
+              "      <td>0.5577</td>\n",
+              "      <td>-0.2479</td>\n",
+              "      <td>-0.6208</td>\n",
+              "      <td>-0.1944</td>\n",
+              "      <td>-1.0120</td>\n",
+              "      <td>-1.0220</td>\n",
+              "      <td>-0.0326</td>\n",
+              "      <td>0.5548</td>\n",
+              "      <td>-0.0921</td>\n",
+              "      <td>1.1830</td>\n",
+              "      <td>0.1530</td>\n",
+              "      <td>0.5574</td>\n",
+              "      <td>-0.4015</td>\n",
+              "      <td>0.1789</td>\n",
+              "      <td>-0.6528</td>\n",
+              "      <td>-0.7969</td>\n",
+              "      <td>0.6342</td>\n",
+              "      <td>0.1778</td>\n",
+              "      <td>-0.3694</td>\n",
+              "      <td>-0.5688</td>\n",
+              "      <td>-1.1360</td>\n",
+              "      <td>-1.1880</td>\n",
+              "      <td>0.6940</td>\n",
+              "      <td>0.4393</td>\n",
+              "      <td>0.2664</td>\n",
+              "      <td>0.1907</td>\n",
+              "      <td>0.1628</td>\n",
+              "      <td>-0.2853</td>\n",
+              "      <td>0.5819</td>\n",
+              "      <td>0.2934</td>\n",
+              "      <td>-0.5584</td>\n",
+              "      <td>-0.0916</td>\n",
+              "      <td>-0.3010</td>\n",
+              "      <td>-0.1537</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.4805</td>\n",
+              "      <td>0.4965</td>\n",
+              "      <td>0.3680</td>\n",
+              "      <td>0.8427</td>\n",
+              "      <td>0.1042</td>\n",
+              "      <td>0.1403</td>\n",
+              "      <td>0.1758</td>\n",
+              "      <td>1.2570</td>\n",
+              "      <td>-0.5979</td>\n",
+              "      <td>1.2250</td>\n",
+              "      <td>-0.0553</td>\n",
+              "      <td>0.7351</td>\n",
+              "      <td>0.5810</td>\n",
+              "      <td>0.9590</td>\n",
+              "      <td>0.2427</td>\n",
+              "      <td>0.0495</td>\n",
+              "      <td>0.4141</td>\n",
+              "      <td>0.8432</td>\n",
+              "      <td>0.6162</td>\n",
+              "      <td>-0.7318</td>\n",
+              "      <td>1.2120</td>\n",
+              "      <td>0.6362</td>\n",
+              "      <td>-0.4427</td>\n",
+              "      <td>0.1288</td>\n",
+              "      <td>1.4840</td>\n",
+              "      <td>0.1799</td>\n",
+              "      <td>0.5367</td>\n",
+              "      <td>-0.1111</td>\n",
+              "      <td>-1.0120</td>\n",
+              "      <td>0.6685</td>\n",
+              "      <td>0.2862</td>\n",
+              "      <td>0.2584</td>\n",
+              "      <td>0.8076</td>\n",
+              "      <td>0.5523</td>\n",
+              "      <td>-0.1912</td>\n",
+              "      <td>0.6584</td>\n",
+              "      <td>-0.3981</td>\n",
+              "      <td>0.2139</td>\n",
+              "      <td>0.3801</td>\n",
+              "      <td>0.4176</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>id_000779bfc</td>\n",
+              "      <td>com_droga</td>\n",
+              "      <td>72</td>\n",
+              "      <td>D1</td>\n",
+              "      <td>df89a8e5a</td>\n",
+              "      <td>0.0743</td>\n",
+              "      <td>0.4087</td>\n",
+              "      <td>0.2991</td>\n",
+              "      <td>0.0604</td>\n",
+              "      <td>1.0190</td>\n",
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+              "      <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",
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+              "      <td>-0.6373</td>\n",
+              "      <td>0.2887</td>\n",
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+              "      <td>0.2539</td>\n",
+              "      <td>0.4443</td>\n",
+              "      <td>0.5932</td>\n",
+              "      <td>0.2031</td>\n",
+              "      <td>0.7639</td>\n",
+              "      <td>0.5499</td>\n",
+              "      <td>-0.3322</td>\n",
+              "      <td>-0.0977</td>\n",
+              "      <td>0.4329</td>\n",
+              "      <td>-0.2782</td>\n",
+              "      <td>0.7827</td>\n",
+              "      <td>0.5934</td>\n",
+              "      <td>0.3402</td>\n",
+              "      <td>0.1499</td>\n",
+              "      <td>0.4420</td>\n",
+              "      <td>0.9366</td>\n",
+              "      <td>0.8193</td>\n",
+              "      <td>-0.4236</td>\n",
+              "      <td>0.3192</td>\n",
+              "      <td>-0.4265</td>\n",
+              "      <td>0.7543</td>\n",
+              "      <td>0.4708</td>\n",
+              "      <td>0.0230</td>\n",
+              "      <td>0.2957</td>\n",
+              "      <td>0.4899</td>\n",
+              "      <td>0.1522</td>\n",
+              "      <td>0.1241</td>\n",
+              "      <td>0.6077</td>\n",
+              "      <td>0.7371</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>id_000a6266a</td>\n",
+              "      <td>com_droga</td>\n",
+              "      <td>48</td>\n",
+              "      <td>D1</td>\n",
+              "      <td>18bb41b2c</td>\n",
+              "      <td>0.6280</td>\n",
+              "      <td>0.5817</td>\n",
+              "      <td>1.5540</td>\n",
+              "      <td>-0.0764</td>\n",
+              "      <td>-0.0323</td>\n",
+              "      <td>1.2390</td>\n",
+              "      <td>0.1715</td>\n",
+              "      <td>0.2155</td>\n",
+              "      <td>0.0065</td>\n",
+              "      <td>1.2300</td>\n",
+              "      <td>-0.4797</td>\n",
+              "      <td>-0.5631</td>\n",
+              "      <td>-0.0366</td>\n",
+              "      <td>-1.8300</td>\n",
+              "      <td>0.6057</td>\n",
+              "      <td>-0.3278</td>\n",
+              "      <td>0.6042</td>\n",
+              "      <td>-0.3075</td>\n",
+              "      <td>-0.1147</td>\n",
+              "      <td>-0.0570</td>\n",
+              "      <td>-0.0799</td>\n",
+              "      <td>-0.8181</td>\n",
+              "      <td>-1.5320</td>\n",
+              "      <td>0.2307</td>\n",
+              "      <td>0.4901</td>\n",
+              "      <td>0.4780</td>\n",
+              "      <td>-1.3970</td>\n",
+              "      <td>4.6240</td>\n",
+              "      <td>-0.0437</td>\n",
+              "      <td>1.2870</td>\n",
+              "      <td>-1.8530</td>\n",
+              "      <td>0.6069</td>\n",
+              "      <td>0.4290</td>\n",
+              "      <td>0.1783</td>\n",
+              "      <td>0.0018</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.5477</td>\n",
+              "      <td>-0.7576</td>\n",
+              "      <td>-0.0444</td>\n",
+              "      <td>0.1894</td>\n",
+              "      <td>-0.0014</td>\n",
+              "      <td>-2.3640</td>\n",
+              "      <td>-0.4682</td>\n",
+              "      <td>0.1210</td>\n",
+              "      <td>-0.5177</td>\n",
+              "      <td>-0.0604</td>\n",
+              "      <td>0.1682</td>\n",
+              "      <td>-0.4436</td>\n",
+              "      <td>0.4963</td>\n",
+              "      <td>0.1363</td>\n",
+              "      <td>0.3335</td>\n",
+              "      <td>0.9760</td>\n",
+              "      <td>-0.0427</td>\n",
+              "      <td>-0.1235</td>\n",
+              "      <td>0.0959</td>\n",
+              "      <td>0.0690</td>\n",
+              "      <td>-0.9416</td>\n",
+              "      <td>-0.7548</td>\n",
+              "      <td>-0.1109</td>\n",
+              "      <td>-0.6272</td>\n",
+              "      <td>0.3019</td>\n",
+              "      <td>0.1172</td>\n",
+              "      <td>0.1093</td>\n",
+              "      <td>-0.3113</td>\n",
+              "      <td>0.3019</td>\n",
+              "      <td>-0.0873</td>\n",
+              "      <td>-0.7250</td>\n",
+              "      <td>-0.6297</td>\n",
+              "      <td>0.6103</td>\n",
+              "      <td>0.0223</td>\n",
+              "      <td>-1.3240</td>\n",
+              "      <td>-0.3174</td>\n",
+              "      <td>-0.6417</td>\n",
+              "      <td>-0.2187</td>\n",
+              "      <td>-1.4080</td>\n",
+              "      <td>0.6931</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>id_0015fd391</td>\n",
+              "      <td>com_droga</td>\n",
+              "      <td>48</td>\n",
+              "      <td>D1</td>\n",
+              "      <td>8c7f86626</td>\n",
+              "      <td>-0.5138</td>\n",
+              "      <td>-0.2491</td>\n",
+              "      <td>-0.2656</td>\n",
+              "      <td>0.5288</td>\n",
+              "      <td>4.0620</td>\n",
+              "      <td>-0.8095</td>\n",
+              "      <td>-1.9590</td>\n",
+              "      <td>0.1792</td>\n",
+              "      <td>-0.1321</td>\n",
+              "      <td>-1.0600</td>\n",
+              "      <td>-0.8269</td>\n",
+              "      <td>-0.3584</td>\n",
+              "      <td>-0.8511</td>\n",
+              "      <td>-0.5844</td>\n",
+              "      <td>-2.5690</td>\n",
+              "      <td>0.8183</td>\n",
+              "      <td>-0.0532</td>\n",
+              "      <td>-0.8554</td>\n",
+              "      <td>0.1160</td>\n",
+              "      <td>-2.3520</td>\n",
+              "      <td>2.1200</td>\n",
+              "      <td>-1.1580</td>\n",
+              "      <td>-0.7191</td>\n",
+              "      <td>-0.8004</td>\n",
+              "      <td>-1.4670</td>\n",
+              "      <td>-0.0107</td>\n",
+              "      <td>-0.8995</td>\n",
+              "      <td>0.2406</td>\n",
+              "      <td>-0.2479</td>\n",
+              "      <td>-1.0890</td>\n",
+              "      <td>-0.7575</td>\n",
+              "      <td>0.0881</td>\n",
+              "      <td>-2.7370</td>\n",
+              "      <td>0.8745</td>\n",
+              "      <td>0.5787</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-2.1220</td>\n",
+              "      <td>-0.3752</td>\n",
+              "      <td>-2.3820</td>\n",
+              "      <td>-3.7350</td>\n",
+              "      <td>-2.9740</td>\n",
+              "      <td>-1.4930</td>\n",
+              "      <td>-1.6600</td>\n",
+              "      <td>-3.1660</td>\n",
+              "      <td>0.2816</td>\n",
+              "      <td>-0.2990</td>\n",
+              "      <td>-1.1870</td>\n",
+              "      <td>-0.5044</td>\n",
+              "      <td>-1.7750</td>\n",
+              "      <td>-1.6120</td>\n",
+              "      <td>-0.9215</td>\n",
+              "      <td>-1.0810</td>\n",
+              "      <td>-3.0520</td>\n",
+              "      <td>-3.4470</td>\n",
+              "      <td>-2.7740</td>\n",
+              "      <td>-1.8460</td>\n",
+              "      <td>-0.5568</td>\n",
+              "      <td>-3.3960</td>\n",
+              "      <td>-2.9510</td>\n",
+              "      <td>-1.1550</td>\n",
+              "      <td>-3.2620</td>\n",
+              "      <td>-1.5390</td>\n",
+              "      <td>-2.4600</td>\n",
+              "      <td>-0.9417</td>\n",
+              "      <td>-1.5550</td>\n",
+              "      <td>0.2431</td>\n",
+              "      <td>-2.0990</td>\n",
+              "      <td>-0.6441</td>\n",
+              "      <td>-5.6300</td>\n",
+              "      <td>-1.3780</td>\n",
+              "      <td>-0.8632</td>\n",
+              "      <td>-1.2880</td>\n",
+              "      <td>-1.6210</td>\n",
+              "      <td>-0.8784</td>\n",
+              "      <td>-0.3876</td>\n",
+              "      <td>-0.8154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>id_001626bd3</td>\n",
+              "      <td>com_droga</td>\n",
+              "      <td>72</td>\n",
+              "      <td>D2</td>\n",
+              "      <td>7cbed3131</td>\n",
+              "      <td>-0.3254</td>\n",
+              "      <td>-0.4009</td>\n",
+              "      <td>0.9700</td>\n",
+              "      <td>0.6919</td>\n",
+              "      <td>1.4180</td>\n",
+              "      <td>-0.8244</td>\n",
+              "      <td>-0.2800</td>\n",
+              "      <td>-0.1498</td>\n",
+              "      <td>-0.8789</td>\n",
+              "      <td>0.8630</td>\n",
+              "      <td>-0.2219</td>\n",
+              "      <td>-0.5121</td>\n",
+              "      <td>-0.9577</td>\n",
+              "      <td>1.1750</td>\n",
+              "      <td>0.2042</td>\n",
+              "      <td>0.1970</td>\n",
+              "      <td>0.1244</td>\n",
+              "      <td>-1.7090</td>\n",
+              "      <td>-0.3543</td>\n",
+              "      <td>-0.5160</td>\n",
+              "      <td>-0.3330</td>\n",
+              "      <td>-0.2685</td>\n",
+              "      <td>0.7649</td>\n",
+              "      <td>0.2057</td>\n",
+              "      <td>1.3720</td>\n",
+              "      <td>0.6835</td>\n",
+              "      <td>0.8056</td>\n",
+              "      <td>-0.3754</td>\n",
+              "      <td>-1.2090</td>\n",
+              "      <td>0.2965</td>\n",
+              "      <td>-0.0712</td>\n",
+              "      <td>0.6389</td>\n",
+              "      <td>0.6674</td>\n",
+              "      <td>-0.0783</td>\n",
+              "      <td>1.1740</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.2274</td>\n",
+              "      <td>0.3215</td>\n",
+              "      <td>0.1535</td>\n",
+              "      <td>-0.4640</td>\n",
+              "      <td>-0.5943</td>\n",
+              "      <td>0.3973</td>\n",
+              "      <td>0.1500</td>\n",
+              "      <td>0.5178</td>\n",
+              "      <td>0.5159</td>\n",
+              "      <td>0.6091</td>\n",
+              "      <td>0.1813</td>\n",
+              "      <td>-0.4249</td>\n",
+              "      <td>0.7832</td>\n",
+              "      <td>0.6529</td>\n",
+              "      <td>0.5648</td>\n",
+              "      <td>0.4817</td>\n",
+              "      <td>0.0587</td>\n",
+              "      <td>0.5303</td>\n",
+              "      <td>0.6376</td>\n",
+              "      <td>-0.3966</td>\n",
+              "      <td>-1.4950</td>\n",
+              "      <td>-0.9625</td>\n",
+              "      <td>-0.0541</td>\n",
+              "      <td>0.6273</td>\n",
+              "      <td>0.4563</td>\n",
+              "      <td>0.0698</td>\n",
+              "      <td>0.8134</td>\n",
+              "      <td>0.1924</td>\n",
+              "      <td>0.6054</td>\n",
+              "      <td>-0.1824</td>\n",
+              "      <td>0.0042</td>\n",
+              "      <td>0.0048</td>\n",
+              "      <td>0.6670</td>\n",
+              "      <td>1.0690</td>\n",
+              "      <td>0.5523</td>\n",
+              "      <td>-0.3031</td>\n",
+              "      <td>0.1094</td>\n",
+              "      <td>0.2885</td>\n",
+              "      <td>-0.3786</td>\n",
+              "      <td>0.7125</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 877 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             id tratamento  tempo dose  ...    c-96    c-97    c-98    c-99\n",
+              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
+              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
+              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
+              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
+              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
+              "\n",
+              "[5 rows x 877 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "06g-Bn1RkGMb"
+      },
+      "source": [
+        "dados.columns = [coluna.replace('-', '') for coluna in dados.columns]\n"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yOcXMh2nkQ0k"
+      },
+      "source": [
+        "# Desafio 01 e 02: Ordenar o gráfico countplot e Melhorar a visualização alterando tamanho da fonte..."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pYiBj0V4kPwS"
+      },
+      "source": [
+        "drogas = dados.droga.value_counts().index[:5]\n",
+        "drogas_mais = dados.query('droga in @drogas')"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 693
+        },
+        "id": "7gsp7ru2khYa",
+        "outputId": "2c5d065f-b05a-47e1-dff0-679993c6d5e7"
+      },
+      "source": [
+        "plt.figure(figsize=(10,7), dpi=100)\n",
+        "ax = sns.countplot(x = 'droga', data=drogas_mais, order = drogas, palette=sns.color_palette('pastel'))\n",
+        "ax.set_title('As Cinco drogas mais estudadas\\n'.upper(),fontsize= 18)\n",
+        "ax.set_ylabel('Quantidade de drogas utilizadas no estudo\\n')\n",
+        "ax.set_xlabel('\\nDrogas mais analisadas')\n",
+        "plt.show()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7hREvmMsmQ4k"
+      },
+      "source": [
+        "# Desafio 03: Plotar os histogramas com seaborn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 684
+        },
+        "id": "MwqikfBVklRe",
+        "outputId": "351ec958-4740-4a38-c400-64f353020248"
+      },
+      "source": [
+        "coluna_analisar = 'g0'\n",
+        "data = dados[coluna_analisar]\n",
+        "plt.figure(figsize=(10,7), dpi=100)\n",
+        "ax = sns.histplot(data=data, bins=200)\n",
+        "ax.set_ylabel('Quantidade de drogas utilizadas no estudo\\n')\n",
+        "ax.set_xlabel(f'\\nGene analisado: {coluna_analisar.upper()}')\n",
+        "ax.set_title(f'Distribuição da expressão gênica do gene ({coluna_analisar}) frente a diferentes drogas\\n'.upper())\n",
+        "plt.show()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1000x700 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Px-LpZAomht0"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X3kcrdAqlSjf"
+      },
+      "source": [
+        "#Desafio 04: Estudar sobre as estatíticas retornadas no .describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tnd1Deg6lUho",
+        "outputId": "b173f125-caab-4047-d7c4-7592d065a82c"
+      },
+      "source": [
+        "dados.loc[:,'g0'].describe()"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    23814.000000\n",
+              "mean         0.248366\n",
+              "std          1.393399\n",
+              "min         -5.513000\n",
+              "25%         -0.473075\n",
+              "50%         -0.008850\n",
+              "75%          0.525700\n",
+              "max         10.000000\n",
+              "Name: g0, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K_RMqR8oltKf"
+      },
+      "source": [
+        "*De acordo com a documentção do 'Pandas' a funcão describe() nos gera estatísticas descritivas que resumem a tendência central, dispersão e formato de distribuição de um Dataset, e o que é importante, excluindo os valores nulos. \n",
+        "Desta forma, essas informações podem nos indicar a qualidade dos dados analisados e também podem ser utilizados na construções de gráfico para auxilar na visualização deste conjunto de dados.*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a_F4DjInwl7N"
+      },
+      "source": [
+        "# Desafio 05: Refletir sobre a manipulação do tamanho das visualizações."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9491y85Hwr80"
+      },
+      "source": [
+        "A manipulação no tamanho das visualizações é importante para ajudar na leitura, pois gráficos muito pequenos deixam os valores e descrições ilegiveis e se forem muito grande podem dificultar no fácil entendimento do que se deseja explicar.\n",
+        "Outro motivo é o tempo de execução do código, que pode levar muito tempo e utilizar memória de processamento de forma desnecessária para plotar um gráfico simples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "id": "ieglFa3gxff8",
+        "outputId": "920d261f-08ca-4131-cb60-09e012cd0c30"
+      },
+      "source": [
+        "print('Gráfico pequeno demais'.upper())\n",
+        "plt.figure(figsize=(3,2), dpi=100)\n",
+        "ax = sns.histplot(data=dados['g0'], bins=200)\n",
+        "ax.set_ylabel('Quantidade de drogas utilizadas no estudo\\n', fontsize=5)\n",
+        "ax.set_xlabel(f'\\nGene analisado: g0', fontsize=5)\n",
+        "ax.set_title(f'Distribuição da expressão gênica do gene (g0) frente a diferentes drogas\\n'.upper(), fontsize=7)\n",
+        "plt.show()"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "GRÁFICO PEQUENO DEMAIS\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 300x200 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 720
+        },
+        "id": "pu5W3BXHySPl",
+        "outputId": "3406e02b-8775-4d09-9440-5897d6fe2ef6"
+      },
+      "source": [
+        "print('Gráfico grande demais'.upper())\n",
+        "plt.figure(figsize=(30,15), dpi=300)\n",
+        "ax = sns.histplot(data=dados['g0'], bins=2300)\n",
+        "ax.set_ylabel('Quantidade de drogas utilizadas no estudo\\n')\n",
+        "ax.set_xlabel(f'\\nGene analisado: g0')\n",
+        "ax.set_title(f'Distribuição da expressão gênica do gene (g0) frente a diferentes drogas\\n'.upper())\n",
+        "plt.show()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "GRÁFICO GRANDE DEMAIS\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 9000x4500 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zb6vHLFqigvO"
+      },
+      "source": [
+        "# Desafio 06: Fazer outras análises com o boxplot e até com o histograma."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TUNWPk7Mi2hg"
+      },
+      "source": [
+        "expressao = dados['g0']"
+      ],
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "d-ucWcqpi9VS"
+      },
+      "source": [
+        "Q1 = expressao.quantile(.25)\n",
+        "Q2 = expressao.median()\n",
+        "Q3 = expressao.quantile(.75)\n",
+        "IIQ = Q3 - Q1\n",
+        "limite_inferior = Q1 - (1.5 * IIQ)\n",
+        "limite_superior = Q3 + (1.5 * IIQ) "
+      ],
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a3gRygEejEcd"
+      },
+      "source": [
+        "selecao = (expressao >= limite_inferior) & (expressao <= limite_superior)\n",
+        "dados_sel_gene = dados[selecao]"
+      ],
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        },
+        "id": "C6L-ieA-ikti",
+        "outputId": "82060eb2-fed8-46e4-f8d4-60b09d2f7fa2"
+      },
+      "source": [
+        "f, axes = plt.subplots(2,1, figsize=(10, 8), sharex=True)\n",
+        "sns.despine(left=True)\n",
+        "\n",
+        "ax_boxplot = sns.boxplot(x=dados_sel_gene['g0'], palette='Set2', ax=axes[0])\n",
+        "ax_boxplot.set_xlabel('')\n",
+        "\n",
+        "ax_hist = sns.histplot(data=dados_sel_gene['g0'], palette='Set2', kde=True, ax=axes[1])\n",
+        "ax_hist.set_xlabel('\\nGene g0')\n",
+        "ax_hist.set_ylabel('Quantidade de drogas utilizadas no estudo\\n')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SliTARXMoZNS"
+      },
+      "source": [
+        "*Realizando algumas manipulações com os dados da expressão do gene (g0) para a retirada dos valores que estão abaixo e acima dos limites inferiores e superiores respectivamente, chamados de 'outliers'.\n",
+        "De acordo com a definição em \"https://www.simplypsychology.org/normal-distribution.html\" e \"https://pt.wikipedia.org/wiki/Distribui%C3%A7%C3%A3o_normal\" uma distribuição normal representa a distribuição probabilitica contínua, onde no centro da curva os fenômenos mais frequentes ocorrem sendo mais raros nas extremidades, o que pode ser observado no gráfico de histograma acima, que indica a ocorrência de um fenômeno natural. E juntamente com o boxplot, podemos também observar que ela obedece aos valores de confiança que podem ser observados pelos quartis do boxplot e confirmados pelos valores de desvio padrão do histograma. verificar figura abaixo.*"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W4ETMc0ytUzC"
+      },
+      "source": [
+        "<img src=\"https://cdn1.byjus.com/wp-content/uploads/2020/10/Box-Plot-and-Whisker-Plot-2.png\">"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gxnqP-QHkYGd"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bh5Olzc2za8M"
+      },
+      "source": [
+        "# Desafio 07: Resumo do que você aprendeu com os dados"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L6UE8dE80Lea"
+      },
+      "source": [
+        "A analise de seus dados e a escolha do melhor tipo de visualização dos mesmo, vai depender de como você deseja transmitir a informações e quais pontos são mais relevantes a serem estudados. Tudo isso vai depender do objetivo do seu estudo e como ele deve ser abordado."
+      ]
+    }
+  ]
+}
\ No newline at end of file