Aula_06_Pos_Ciencia_De_Dados.ipynb

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    "colab": {
      "name": "Aula 06 - Pos_Ciencia_De_Dados.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "wM9AXJjYmCU2"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4uSUnSA1bJS"
      },
      "source": [
        "# **Turma de Pós-Graduação de Ciência de Dados**\n",
        "\n",
        "**Disciplina: Linguagem de Programação Python**\n",
        "\n",
        "**prof: Sérgio Assunção Monteiro**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzyzXZSl1fXz"
      },
      "source": [
        "# **Aula 06**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7UxFnqON1vud"
      },
      "source": [
        "#**Importação, Manipulação e Visualização de Dados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
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              "status_text": ""
            }
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          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "ES255U9GboB1",
        "outputId": "ae04a5cb-ed82-431f-989d-3555ea3ea517"
      },
      "source": [
        "from google.colab import files\n",
        "arquivo = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-01bb1b89-d968-4bf0-94ca-0e00d6be6322\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-01bb1b89-d968-4bf0-94ca-0e00d6be6322\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving planilha_exemplo.xlsx to planilha_exemplo.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "yajx1LQJcCPz",
        "outputId": "c5e2df39-222f-4512-eb04-45f3b81d0037"
      },
      "source": [
        "nome=list(arquivo.keys())[0]\n",
        "nome"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'planilha_exemplo.xlsx'"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTQhMvFRgRh-"
      },
      "source": [
        "**Transformar os dados em um Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpO7thhEbxHS"
      },
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "df = pd.read_excel(io.BytesIO(arquivo[nome]))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86l8Dk0Tgjh1"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5xKpYC4zgyyc",
        "outputId": "4fdf6c34-3ba7-4b1c-b82a-3936aa86b54c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idade</th>\n",
              "      <th>Renda Anual (k$)</th>\n",
              "      <th>Pontuação de gastos (1-100)</th>\n",
              "      <th>Cluster</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b1a9640-ca2e-450c-b313-ced855883d8f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2b1a9640-ca2e-450c-b313-ced855883d8f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Idade  Renda Anual (k$)  Pontuação de gastos (1-100)  Cluster\n",
              "0     19                15                           39        4\n",
              "1     21                15                           81        3\n",
              "2     20                16                            6        4\n",
              "3     23                16                           77        3\n",
              "4     31                17                           40        4"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPgOKmNSgoqr",
        "outputId": "8b858f4f-8220-493c-818c-34b46291275e"
      },
      "source": [
        "ncluster = df['Cluster'].max() + 1\n",
        "print(ncluster)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMkhu_Szg84T"
      },
      "source": [
        "y = np.zeros(ncluster)\n",
        "lstGrupo=[]\n",
        "for i in range(ncluster):\n",
        "  total = df.loc[df['Cluster'] == i,'Cluster'].count()\n",
        "  y[i]=total\n",
        "  lstGrupo.append('C_'+str(i))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cou2IZZRVdSz",
        "outputId": "b4294b94-aa0a-48a6-cba4-39dca2b45746"
      },
      "source": [
        "lstGrupo"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['C_0', 'C_1', 'C_2', 'C_3', 'C_4']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "_1F8Lc89hJxk",
        "outputId": "e734909c-cc58-45e7-8ade-ec5a20b2ac3e"
      },
      "source": [
        "explosao = [0.1, 0.1, 0.1, 0.1, 0.1]\n",
        "plt.title('Resultado do Agrupamento')\n",
        "plt.xlabel('Clusters')\n",
        "plt.ylabel('Quantidade de Pessoas')\n",
        "plt.pie(y, labels=lstGrupo,\n",
        "        autopct='%1.0f%%',\n",
        "        pctdistance=0.8,\n",
        "        labeldistance=1.2,\n",
        "        shadow=True,\n",
        "        explode=explosao)\n",
        "\n",
        "plt.legend(labels=lstGrupo,\n",
        "           loc=\"best\")\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5X_RmzM_Xlj_"
      },
      "source": [
        "**Gráfico de barra com Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-njsk9L1Xta3"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IP2fi-G4Xyrm"
      },
      "source": [
        "dados = np.random.rand(10, 4)\n",
        "df = pd.DataFrame(dados, \n",
        "                  columns=('coluna_1', 'coluna_2', 'coluna_3', 'coluna_4'))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JFCnGa-3X8rO",
        "outputId": "221546a8-a423-46f8-8f7c-25f19bea0ba7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-19db6c64-d3f7-4c38-a38a-40f70105f062\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>coluna_1</th>\n",
              "      <th>coluna_2</th>\n",
              "      <th>coluna_3</th>\n",
              "      <th>coluna_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.519845</td>\n",
              "      <td>0.799045</td>\n",
              "      <td>0.598666</td>\n",
              "      <td>0.946039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.416048</td>\n",
              "      <td>0.410530</td>\n",
              "      <td>0.194199</td>\n",
              "      <td>0.900220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.374183</td>\n",
              "      <td>0.745390</td>\n",
              "      <td>0.812219</td>\n",
              "      <td>0.212210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.274557</td>\n",
              "      <td>0.952946</td>\n",
              "      <td>0.193796</td>\n",
              "      <td>0.212393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.355985</td>\n",
              "      <td>0.184283</td>\n",
              "      <td>0.771151</td>\n",
              "      <td>0.422163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19db6c64-d3f7-4c38-a38a-40f70105f062')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-19db6c64-d3f7-4c38-a38a-40f70105f062 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-19db6c64-d3f7-4c38-a38a-40f70105f062');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   coluna_1  coluna_2  coluna_3  coluna_4\n",
              "0  0.519845  0.799045  0.598666  0.946039\n",
              "1  0.416048  0.410530  0.194199  0.900220\n",
              "2  0.374183  0.745390  0.812219  0.212210\n",
              "3  0.274557  0.952946  0.193796  0.212393\n",
              "4  0.355985  0.184283  0.771151  0.422163"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "-M7A2B3fYEY2",
        "outputId": "04f51e17-238b-4908-d4c9-c3829a26ab6a"
      },
      "source": [
        "df.plot(kind= 'bar')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6587ebdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "pPajYI29ZVpk",
        "outputId": "6b3103b5-76a1-44c1-e51e-105a44eedecf"
      },
      "source": [
        "df.plot.bar(stacked=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6588312d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "LI2wUW7VZgsN",
        "outputId": "f005121e-cbfd-4615-935a-aafc733e63e6"
      },
      "source": [
        "df.plot.barh(stacked=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6585fe5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "96OlyDWbZmo_",
        "outputId": "7365410a-3ba7-42c2-a553-87aabb5aa366"
      },
      "source": [
        "df.plot.hist()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6584edbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "PknMwtNaZ2VE",
        "outputId": "b6b8a62a-3183-4811-e77c-54cdec39f194"
      },
      "source": [
        "df.plot.hist(stacked=True, bins=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6583fa990>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "7b5k42_cZ_EV",
        "outputId": "6a2fe852-0ad0-43ba-fd0b-35bb5388ee33"
      },
      "source": [
        "df.plot.box()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe65825d410>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cQ0nuYfDBv4/BVSxnleTXGHxJ8nTgc0kWqmrqr8yY1v4E/ozBGOenBhnEB6rqlo6/1sRMaX8GuC3JU4bLh4BfXcOvNRFT2pdTwblcJKkRLQ25SNK61syQy6Ql+Rtg24rNb6yqA5OoZ9bZn/2yP/szzX3pkIskNcIhF0lqhIEuSY0w0CWpEQa6JDXi/wGXL5TamIeZ0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "I7erSmVOaLF2",
        "outputId": "8943aab7-6f30-40fa-d282-30fd75ae2e01"
      },
      "source": [
        "df.plot.box(vert=False, positions=[1, 2, 3, 4])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6583eed10>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "AqqdexvGZGoP",
        "outputId": "eb1c8bc1-0f2c-4eb2-c028-7bb2b51ddeb6"
      },
      "source": [
        "df.plot(subplots=True, figsize=(8, 8))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe65819b410>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe65814b490>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe6581057d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe65813bb10>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "ICzgwsSIZfpF",
        "outputId": "38cebfb8-8cc1-46dd-ad85-f3b098cdfc7b"
      },
      "source": [
        "df.plot.area()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe658031f50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "9sCmzKX8alUT",
        "outputId": "8cd27b2d-c520-46c0-8722-87d0ec445854"
      },
      "source": [
        "df.plot.area(stacked=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe657fc2b50>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXbO8Hr_h_y1"
      },
      "source": [
        "**Seaborn**\n",
        "\n",
        "url: https://seaborn.pydata.org/\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmvE2Xi4iFWz"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_theme(style=\"darkgrid\")\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBRIxhYwjB_A"
      },
      "source": [
        "df = pd.DataFrame(dict(tempo=np.arange(500),\n",
        "                       valor=np.random.randn(500).cumsum()))\n"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "NByM8Qf-Vu0R",
        "outputId": "1d722da3-c92a-4e0f-edd0-34989e4e2ba5"
      },
      "source": [
        "g = sns.relplot(x=\"tempo\", \n",
        "                y=\"valor\", \n",
        "                kind=\"line\", \n",
        "                data=df)\n",
        "\n",
        "g.figure.autofmt_xdate()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-WHKQcnVrM4"
      },
      "source": [
        "df = pd.DataFrame(dict(tempo=pd.date_range(\"2020-1-1\", \n",
        "                                           periods=500),\n",
        "                       valor=np.random.randn(500).cumsum()))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "tzOS4D_FkHJR",
        "outputId": "862bb22a-1371-4db1-db19-50df7992afea"
      },
      "source": [
        "g = sns.relplot(x=\"tempo\", \n",
        "                y=\"valor\", \n",
        "                kind=\"line\", \n",
        "                data=df)\n",
        "g.figure.autofmt_xdate()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JIlZTGVK40RMtz+fAyY6w34O9EFp/gIva+rCqzISy0IBFPuoqEkPu0aa6FJeEliDyCGl8TQo92kVzxkg/F5iEDTZ5W0MACAaTF1qvPxh1gCTDMLjy/GEAkJIy4MvPqYFWrcT/u24K5p03pNfnk0NCSxB5RDo82poKC35ywXAAwCCrCUoFg7qW8NzW3oQOXN4gTDp11OeGpHDDakxNMZ751SwMH1SAsigDHnsDCS1B5BHuUMJ/KoUWAM4YWYpzxldg/syhUXvcBnrh0To9ARj10dcrpnnlOrk3xYwgiLTh8YUGGKYwdAAAZUUGLJLNEGu1h28mBdnkY6gub0DqFxuJmMkgbwiei5DQEkQe4famx6OVM7a6CIdP2cIavyS7GRYIsvAHOBhjhA4A4Mm7zgsrZshFcnt1BEGkFLs7AL2298n93TF7yiBMHV2Gm37YuUmWbOhAHJYYy6MFAINOndRUhUxCQksQeUSb3Zv2uKbFoMFt88ZjyigrZkyoBJC8R+sKpVkZuxHavgAJLUHkEa02L0oyuIE063tC+9OkhTYU6jCmOKacaUhoCSKPaLVnVmjVoRBFIJjcZpgn1Mg7lZ20sgEJLUHkCR5fEC5vEMWW3o0YTwQxFpysRyuOydHleAy2J0hoCSJPEIsIBpT2flZYvKhUvRTaKPPC+iIktASRJ5xqFspiB1oTH/mULFLoIEmh9ZJHSxBEX+J0swtatRKlBZmL0Yqjv5NN7/L5hfSuVPTOzSYktASRJ9S1ujCg1ABFb+a9JIjoiSY7ndYbYKFSKtKa95sJ+vbqCYKIm7oWFwaUZC4+CwiNwTUqhVSRliheP9vnwwYACS1B5AVubxAdTj8qM7gRJmLUq+EKVXglio+EliCIvkJ9ayjjIMMeLSBMc3Al6dH6/F3H2PRFSGgJIg/oTO1KbZ/VeDBqVVIf3ETxBljo+vhGGEBCSxB5QX2rGyqlAqUFmW8naNSrcbrFhRMNjoRf6/UHyaMlCKJvcKTOhqoyY9KTaHsDy/FwegL4wwtfJ1y44PWxfb78FiChJYh+j8/P4midHaOri7JiXy1LzUo0hOD2BdPaOzdTkNASRD+nucMDluNRXZ66+VqJcN0lo3D+ZKGLlzgcMl7c3iB5tARB5D6+YHbLWAuMGkweUQoACWUfsBwHX4CFoY+3SARIaAmi3+MPCHFRjSp7m0oGrdC425NA6ECcb0ahA4Igch5/qANWNvsFiMMg3b6g1JGrJ8RqMgodJMHq1asxe/ZsjBo1CgcPHpQenz17Ni699FLMnTsXc+fOxebNmzO9NILol/hDDV006uz5VaJXuuGrk7j1fz5Dq80b9Tie5/Hsv/bgq32NnR5tPwgdZPwKLrzwQtxwww249tpruzz3+OOPY+TIkZleEkH0a3LBoxXF8li9kEtb2+zE6OHWLsc127z4al8TlApGmjfWH0IHGb+CqVOnZtokQeQ1/hxonq1RKaBSMgiywkibpjZ31OP2HW8DALQ7fHD7+scYGyALQtsdS5YsAc/zmDJlCn75y1/CYrEk9PqSksQbGlut2Ul5Ifu5YT9ba2h3ePHNviZMN2rTbl+lETaiBlRYYNB1nSabqeuvrrTgyCkbAKDNFYhq+2iD0Jzc5gpAE1prZYUF1tL0NCvP1LXnjNC+8sorqKyshN/vxwMPPIAVK1ZgzZo1CZ2jtdUJjot/CJzVakZzc+JlgamC7GfXfjbX8L8fHsTGb07hRtdYnDe+Iq222jsE79Fuc8PlCI/TZvL6ywr0ktCearQDQJhtnuex62CT8HiHBy2tgui6HV4088kNd+yOVF97d6KdM1kHlZVCPEaj0WDhwoXYvn17lldEEOnDGdpRd3qS62qVCL4gC5WSgVKR3T/3qy4YhhsuHYUpI61od/i6PO8PcLC7Ayg0aRBkObTYhQ0z6nWQItxuNxwO4ZOF53m89957GDNmTJZXRRDpQyxFTXbyQCL4A1xWc2hFCk1anD95IIosWrTZfeAjvFTxQ0dsfGN3+gH0/TE2QBZCB/fffz8++OADtLS04MYbb0RhYSGeffZZ3HHHHWBZFhzHYdiwYVi2bFmml0YQGUOskPImWJKaDP4Am9XUrkiKzTr4AmyXvgei0BZbtMBpwO4OQKNWZHT0TrrIuNDee++9uPfee7s8vn79+kwvhSCyRqRH6/EF8fm39Zg9ZVDKhcUf5HLKKywyawEALTYPDMrOaxXDKcUWYXikw+3vF71ogRwJHRBEPhFkObTZhRilJ+TRfnOgGf+78RBONqZ+Y8rrC2Y1tSsSo17w71wR8Wnx9+KQEDvc/pz6gOgNJLQEkWEee32XVIbqDY3TtruFeGRzR/SKqd5gc/lhMWpSft5k0WtC5bgxQweCR2t3B/rFvDCAhJYgMkqb3Yt9J9ql38UYrd0lCG1LhyflNm0uPwpzSGh1oQKEyAYzotCKoYVAkOsXGQcACS1BZJRmmZAyTGeHKofo0cboAZAsHM/D7vKjwKRN6Xl7g1hSG9ky0e0VQhzykluK0RIEkTDtTiE2u/LmM3Hu+MrO0EGaPFqnOwCW41FgyiGPNuSlRoYOPL4g9FplWFyWYrQEQSRMh0MQ1CKzFlqNUso6sLsF7645xULbERL2whzyaLUaJRgAbl+4RysIrSosFS0bwyTTAQktQWSQDqcPGpUCeq0KOo0SHm8Abm9A2gxrtXvBpbDc1BbylAtyKEarYBjooowg9/hZGLSqsOKKgVZjppeXFkhoCSKDdDh9KDRrwTAMpo4qA8cDn+w4Dac7AJNejSDLoyNKeWpv7AFAYQ6FDgBAr1VKjb1FPL4gdFoVVLLcWhJagiASpsPhk77GV1eYYdKrcarZBZbjUVMpNCVptaduQ8wWKmPNpc0wQEjxih6jVYGRFWxUWdPTtSvTkNASRAbpcPql9CUAMOjVaAj1ZhVFRdwYSwU2px96rTKnChYAQKdVdknv8viC0Eekc/WXzbCcaZNIEP0dnufR7vThe7Kv8SadGnUtQjvAQWkQ2g6XDwXG3PJmAaGZd2R6l8fHSk2+584YIr0f/QESWoLIEG5fEIEgF5YBYNSrpcyDAaVCPNKWQqF15FhVmIheo0K7o3PKAsfx8AXChbY/QaEDgsgQ4iZXuNB2+jpFZi1MerWU6pUse461ojEUjnD5gjDm4HBDvVYJjyy9yxPKJ+4PY2ui0T+viiDSDM/zYZs28SCWmFoMneNkjHrhZwaASa+Gxajpdejg0b/vAgCsXTobbm8Qxijja7KNLrQZdvi0DW5vEANKDQDQJUbbXyChJYgEaXf48PD/boe1UI/FV02Me3KB0yN4bfK5XaLQmgxqKBQMikwabD/YjDc/PYIfnz8s4bXJm2k/985etDt8OTmu26BVwetnseqlb8Ie768eLYUOCCJBvt7XiMZ2D/Yca0NTe/yVXGLeqPyrvOhtmkKCay0UKqHe+/JEwuv65kAzmmSVZV/saRBs6HPQo40hqPoc/FBIBf3zqggijThlu+WRuaDd4QodKxe+klCJqbghJrYITBSXN4Cn/vmtMJ0ggpyM0cYIEYgtFPsb5NESRIK4ZOLqSkhoA0L5qUxkxg4pBgBpWGEywxV4nsfLHxwEAKmhuByVMvf+zGOFCPTa/hmjzb3/AYLIAO0OHxat/gSHatt7PliGP8DiWJ1d+j2yMUp3uL1BGHThlU+DyoRc0fMmClOgzxlfmdB6AKCp3YP/7G2M+XwmBkAmSqwQAcVoCaIf8e3RVnA8j//74nhCr3vh//bjeIND+jqeWOgg0GVjimEY/HnJLPz0B6MBCCleV8wcCkBofB0Pxxrs3T4/dZQ17jVmigJD9NzevBZajuOwdetW+P2pS6QmiGwSZAURUyb4tfrgqY6w3+MV2kCQxclGJ8yGrhtTapUybCCjGFoQe9XG4tWNh/DIqztwssEJlZLB9LHlYc+rlAye+80FScd900ms/rgaVf/0/eK6KoVCgdtuuw0aTe5VmBBEMgRZIQ1K3ikqHswhT8wXEIT6H5uOStMRumP/yQ40tLlx0ZSqHo/tFNruv/J/uK0W+060o8Plg9mgQWlBuKCWFuhzdlS3KUYmRKK5yX2FuD8+pk2bhp07d6ZzLQSRMdiQR5voRpElJLSiRwwA9a3uWIdLiJMTRlYV9nisuPMeb2z18CkbNGolhg6whD1+wfcGxvX6bMAwTJcPueU3TsvSatJP3AGRAQMG4JZbbsGFF16IioqKsE+exYsXp2VxBNEbapucONHgwIyJXTeYRKFUJ/hVVakQ7vsfTB+MD76uBcvF16S7xeaFSsnENVImVujA5Q3gmwPNOG9iJRiGgcUglOu22LyoKjNhfCiD4exx5bjh0tE517ErEr1WBYes3Li/9J6NRtxC6/P5cNFFFwEAGhtj73ASRK6wbO1XABBVaANS6CAxofX6gxg+qABXXTAck0eU4sGXt8e1adVi86LEoovrq7wuhkf78gcH8Z+9jagqM2FIpSXUQlAQKo1KAbVKiccXnwedRpmTKV2RjKouxrZ9nVoSb4VdXyRuoX3wwQfTuQ6CSCny2Vscx0OhCBc4f0AQMXkIAABONjrwztYT+PnlY6OKldfPwhTa0BJHrviDPX/Fb3N4496UEnNJPb5wj9YWmpYg9kyQC7HomceKfeYid19zBp5+fQc+D1Ww9WcS+gg5fvw4nnzySdx333148skncfz48YQNrl69GrNnz8aoUaNw8OBB6fFjx45hwYIFuOSSS7BgwYKkzk0QIr95dqv0c7RYp/i1PNIbXb7ua2zb3xSztNYXYCWPUxUSt3g8Woc7EDXjIBqiWDo9AdQ2OdFq82LnoRaoQ8IudgHzBTqvqy82yLYYNbh5zthsLyMjxC20H3/8MebPn49jx46hoKAAx44dw5VXXomPPvooIYMXXnghXnnlFQwcGB6oX7ZsGRYuXIgNGzZg4cKFuO+++xI6L0HEIlqalCi+/kB0b1T0GqO9ToyhahIQWpcnELe3KZboNrS5sWztV/j1M1/g8bd2wxe6jla7FyzHIRDkIPrpicaac4lVPz8Lv1n4vWwvI63EHTp47LHH8PTTT+Oss86SHvvPf/6DlStX4sILL4zb4NSpU7s81trair1792LdunUAgDlz5mDlypVoa2tDcXFx3OcmiGj4ooipmP8qF0mfzPN1xRTaoCS0orj5exBajuPh9gbjFloxZLFx26mwxw+esgEQymx9fsFmgUmDDqcfKRycm3Eqig2oKDZkexlpJe6PwYaGhi4iOWXKFDQ09D6+Ul9fj/LyciiVwg2sVCpRVlaG+vr6Xp+bIKKFDjpCQwvlQmuT5cNG82g5nk/Ko3X7guCBlPWFdXkD0oeHOKaGizP7gcgOcXu0o0ePxtq1a/Hzn/9cemzdunUYM2ZMWhaWDCUlic8YslrNaVgJ2c+m/Ude2hb2+78+P44Hbj0XtY0O/GX9t/jtT6dJ42L8QVZaQ6urU1x5haLL2mobHeB5YFhVEaxWMwKhTTCNVtXtdfibhZlgleXmqMcl+h4EOcAQmtJgLTbgRKMDKrUy6fcym/dAf7z/ohG30C5fvhy33nor/va3v6GyshL19fXQ6/V49tlne72IyspKNDY2gmVZKJVKsCyLpqYmVFYm1mCjtdWZ0Ce71WpGc7Mj0eWmDLKfevt2tx+bdp4Oe2z34RY0Ntrx7Fu7sftIKzZuPS5Vc9U2OrB520mMri7CidOd5bWNLc4ua9u2R/iGZTVr0dzsEKYsAOiwebq9jjWh5tZcgO1yXDLvgc3hRX2j0N9Apxa8aq83kNR7mc17oL/df92JdtxCO2zYMLz33nvYuXMnmpqaUFZWhkmTJkGt7v3XoZKSEowZMwbvvPMO5s6di3feeQdjxoyh+CyREHuOtUpjXCLxB1kph/VUyMMEgNpGJx5+dQeevGtm2AiZaKGDE40OaDVKVJQI8USGYaBWKXqM0R4+LcRW4806AICbLxuD59/dBwCYd94QrN98THrO7QvCGUr0Lwp5tlxfDtLmAQm1ylGpVFE3sxLh/vvvxwcffICWlhbceOONKCwsxLvvvovly5dj6dKlePrpp2GxWLB69epe2SHyj6OnY3ex8gc5STy/Pdra5fldR1okoZ2F6BsAACAASURBVK0oNkQV2sY2NyqKDGFFB2qVIq6sA5NejeqK+L+mnjuhUhLaOefUYMvuerTYvCgwauDxBdFi8wIAyoqExuEUo81tuhXaWbNmxdXk4dNPP43b4L333ot77723y+PDhg3DG2+8Efd5CCISpax2/uJpVWA5Hh99I+zcb9vfJHmWp5pd0KgVUCkUcIeKAmxOP2xuP4w6FQpNmuhC2+7GkMrwfgKC0MYuWBBF+PvTqpJu8KJgGGkWWHmRHodP29Fi80KpYFBkFj3apE5NZIhuhfaRRx7J1DoIotfIq7/OGV+BweVmsByPT3ecxokGIRYnTpkdPrAAe493Nv0OBFnYXX5YjBqY9GqcbnGFnTvIcmixeXHW2Iqwx3vyaMU5YaYkxsks+9k01LcJ6xB1tKzIgIOnbKhrcaHIrMVAa3jjcCI36fZ//8wzz8zUOgii18g9RrGd4fghxfh0x2m0O31QKhhUl5vx7dFWmPRq3HDJKNS1efDxtlr4gxzsLj8KQkIb6dHaQrmqJRGtCDUqZbcxWvE8yQxIrK4wS+GGkVWF+PK7RlhDoYJdh1swbkgxCowarF06O+FzE5kloY/Zffv2Ydu2bWhvbw8ba0zdu4hcQF7lZdILt7YmtCvf4fDDoFNJfQT0WhXO/95AWK1mbN55GoGQ0FZXmGHUq+HyBMHxvCTe9lCWgiViMoBapYhaECESbSBjMvzs0tG4ZNpgqYcDD+CSMwf36pxE5oi7YOHvf/87rrnmGnz55Zf461//ioMHD2LdunU4efJkOtdHEF3geB4s19WL9AbkTVbEooJQfwCnDwatShqVIh+ZIn79t7v9sBg0MOvV4Hg+rKmLuFFmNoYLpsmgjllFBnR6tKZeFito1EpUV5jDWgnG09uWyA3iFtrnnnsOzz33HJ566inodDo89dRT+NOf/gSVqn/O+CFyl/e2nsDydV93edwXpQJMLJN1egIw6NRSeWuk0Lq8AXh8LCxGjeR9ysMHotBGzroy6zWwu2ILrSsUo03VyO9yWalqX+5vkG/E/T/V2toqpXYpFApwHIdZs2bhk08+SdviCCIap5qdUbtrRRNaeVcro04l7SrJm2JrVAq02oV0KbnQujwyj9YterThQmsxquHwiOW8LFa/sh3H6jvTzDw+YU2xpr4mioJhMO+8Ibj+klEpOR+RGeIW2oqKCtTW1gIAampq8NFHH2Hbtm0pKVggiESwu/wIBLku4QNvgIVSwWDlounSY/JhfwadCnxIaeWZVmqVQspLtRg1nRNufXKPNgCtRtllaoHFoIE/wMHnZ3GyyYkDtR3424YD0vNi+EEcT5MKfnTukJweU0N0Je7//UWLFuHo0aOoqqrCbbfdhsWLFyMQCOB3v/tdOtdHEF2wh6qifH4OBl2nkPr8LAaXmzCwtDOOGe7RqqU0KXlGq1qlhM0ppFEVGDWSOMsn3Nrd/qgjssUm4Ha3H7yo+7KcVo8vCK1G2aXxOJFfxC20+/btw+WXXw5AKGT46quvEAgEYDT23zk/RG4ixkt9ARYG2Vdyjz/YxeOUe7QmvRrOUMyUiajuErEYNJIourzhm2GRG2Hi8QBgc/klD5iXKa3bF4Re0/eachOpJaHvM7fddhsMBgPmzJmDyy+/HEOGDEnXuggiKizXWUorT6v624YDOHLajoumDgo7Xi6iZoMas6cMgsMdwDnjOwsP5GJsMaql3q5iscHmXXXYd6Id3xtR2mU94uZUfatL2mjjIzxa+cYbkZ/EHaP93e9+h02bNmHZsmWor6/HT37yE8yfP19q1k0QmUA+NVW++fXpDqFjl1xAgfDhi2aDBgVGDW6bN75L1oH4r1qlhFqlgErJSKGDdf+3H0D0eVxlhXpoVAqcanJFTfPy+IIwkNDmPQnlhygUCpx77rl48MEH8c4776CwsBAPP/xwutZGEF2Qd9jyyQYsMgAumjIINRWWGK+M3T1LFFox7MAwDAw6dVjoQG5PjkLBYKDViFPNTul4eSct8mgJIMHQgdvtxocffoh3330XX331FaZNm4aHHnooXWsjiC7YZVMQxMkJHU4feACDyrpv/B5rlIwotDpNeCrYiUYHbnroY+mxWE1hqspM2LSrHvtOCL0T5IUOHh+L0gJ9t+si+j9xC+2dd96JzZs3Y+zYsbjsssvw0EMPUb9YIuM4ZMUBYslth0MQX7GTVSQatQL+ACf1P+jyfKh6TCsT2mKzFt/Jms7UVJix4MIRUV8/sDRc4NvsPuw70Y4x1UXk0RIAEhDaCRMmYOnSpRgwYEA610MQ3WKThQ7qWlxgOQ5tDiEHVmyCHcntV0zAu18chyVK1gAg82hlGQsDraYwob1y1jAUGKMLdWVp18GCj7y6A3fMn0AxWgJAAkJ7yy23pHMdBBEXDlnoYP2WYzhQ2yF9ZbfEEMIJQ0swYWhJzHOKIQVGlus6oDQ8bVHXTYrWuJpiDBtgwZG68MbjT/zjWwCQGtkQ+QsVSxN9Bo7ncaC2A8UWrVSUIIosABj1yXmO4iZZINBZaRbZ4FvbjdAyDIMzRloBAJYoG24UOiBIaIk+Q32LC0fr7Pj+1CqsXDQdMyKaXSsVyd3OYuzWL5uUMMgav0cLQCqcKLLoujxHQkuQ0BJ9BjF9alBoqsCZo8tScl7Jo5U18GYYBnf+eKL0u66HXgWimBYYNfjJBcPDnqMYLUFCS/QZpAYtIeEaP7QEj90xo9fn7fRow5vUTB7eWQnWk0cretMMgEvOrAoTV/JoCRJaos/QKbSdomfu5eQCoHMzrLu+L/IKs+iIXcEYMAyDNbefIz1DQkvQHUD0GUShlXuLYgMYZS+6Yxl1Klx+Tg2m9SIUMWpwEUoLdPjRjBoA4f1uKeuAIKEl+gzuiNCByL03TI2Z4xoPDMPgiplDe7U2k16Nh2/t9GIZhsGlZw7GZ7vqUGCMnt9L5A8ktESfweMTGntHjnAZOiB2f4Ns8pPZw/HjC4bFLN0l8gcSWqLP4A6VszIZFK7b5o2X5n4lA4ksAZDQEn2IbJSzTk1RChmR31DWAdFncHkCSVd/EUQ2yam7dvbs2dBoNNBqhc2DJUuW4Lzzzsvyqohsw3E8th1oQofTRy0HiT5JTgktADz++OMYOXJktpdB5BCf7arDS6HJssMGFmR5NQSROBQ6yFPqW134xyeHsr2MuJBPVehNGhdBZIuc82iXLFkCnucxZcoU/PKXv4TFkpupO32dR/++E612H6YMLw2bJJuLyIsRSGiJvkhO/YW98sorqKyshN/vxwMPPIAVK1ZgzZo1cb++pKT7USbRsFrNCb8mlWTLvi/UElBn1MJa3Nm4uqndjV888jHuuX4aJo0ohVqV3qqmeK6fl3XlqhpQkPL3LF/vgVywny/XnlNCW1kptL3TaDRYuHAhbr311oRe39rqBMfxPR8Ywmo1o7nZkZCNVJJN+2Lp6snT7VCwne0B39t6HB4fiz889yUAYO3S2Wlbg9VqxneHmgAI02Rj0dTilH4eUKhL6XuWz/dAtu33t2vvTrRzJkbrdrvhcAgXzfM83nvvPYwZMybLq+q/qJWC0DojRmQfOmXL6DqWPrsVS5/d2u0xdncABq0KD9wyHQZd75vIEESmyRmPtrW1FXfccQdYlgXHcRg2bBiWLVuW7WX1W5ShblSi0B4+bcOql76BRp3dz942uxcWoyasW5bN6cPwQQWoLDF280qCyF1yRmirqqqwfv36bC8jb1CHhMzlCeIPL3yNEw3Ctwl/gIO1UIfmDmHgYZDl4mgR2Ht8ARY+P4slT3+BS6cPlppn8zyPZpsHIwYVpn0NBJEuciZ0QGSObfubcLrFBUDwaEWRFRlVVST9HIhohp0uWjo8aGhzAwAOnOyQHnd6AvD4WJQVUaEC0Xchoc1Dnl6/R/o5MkYLAGNqiqTerAE2fUIr37hsc/jQYvMACE/hamoXHrOS0BJ9GBLaPMfm9HV5bGCpEeOGFAMAghEeLc/Hn9XRE2IjbwDw+Vk0tAmiapJNkm3qEB7rLiuBIHIdEto8wx9gw34XY7EzJnROlK0qM0k9X+Whg12HW3Dz6k/Q1O5OyVpcMm/aH2Sl8wZlXnRzuwcMAGth1+myBNFXyJnNMCIzONzhoQLRYxxTU4RLpg8Gz/FgGEbaLJMPLHzvyxMAgNomJ8qKDOgt8j6v/gCHxpBH6/V1fhg0dXhQZNGmvXCCINIJCW2eIY6DGTGoADyAw6G8WYNWhYGlnelT0TxaMV4qjv3u9Vpk5/EFWDSEPFqvv/PxpnYPhQ2IPg+FDvIMd8iLnDdjCEbXFEuPR/Y76BTaTu+SDW1edTi6xnWTQe7Rtti88PkFWz5ZeMPm8qHQTDO3iL4NCW2eIXqjBp0aFkPn7n7k5AJN6Ku6mHXA87y0ebV+y7GUpH25ZTHaVptX+tnr7xRan5+FTk1hA6JvQ0KbZ4hepEGnQoHMUywwhXuNkaEDjy8oebQA8F9rPu31pph8M8zmErzkIrM2TGi9ARZaDQkt0bchoc0zxLioUadCdUVnC0qTPryHgEomtBzH41SzUOAwZZRVOuajb073ai0u2Vo6nELPWUFog9h5uAXNHR74Axy05NESfRzaDMszXN4gGAA6rQpWa+xNJrlH++/Pj+Hfnx8HAMycNADfHGgGEJ7vmgxubwAqJQOjTo3mULFCZbEBR+vsePzN3dJx5NESfR3yaPMMtzcAg04FBcPAoFNjxKAC/Pj8YV2OE9O7AkEOu4+0So+bZeLq9fUu+8DlFcaHa9QKiHUQwwZ1HVVDHi3R1yGhzTPaHT4UyuKxv71uCn54VnWX48QuXoFg+Ff3AqMWD9wyHUD4iJlkcHuE9oca2flHRJkJRkJL9HUodJBntNl9KLb0XGUlZh14A2yYEBaYNChitKipMMPu7tonIRFc3oDg0YbCFEoFg8rSrq0QSWiJvg55tHlGq92LEkvPealqlQJKBQOvPxjWo1bBCA3DLUYN9p1oD8uFTRSXRxBaY2gjjuV4KBgG54yvCDtORzFaoo9DQptH+AMsnJ5AXB4tAOi1Knj9bNTxQOeMr0CQ5bBtf1PS63G4/TDq1Vh4kTBeXhWa+nDTZWPwpztnSMdpyKMl+jgUOsgxDp+ywaBTYUCUr9C95WSjMHurvDi+PgU6jRJeX1DKa739ignSc2eMFNK8Xnz/ADRqJc4eVxH1HN3RZvdhzOAiFJm1ePKu8+AJ9ThQMAzMsmIK8miJvg55tDnGqpe/wb3P/QeAILo7Djan7Nw7D7dAqWAwrqao54MB6DSCR+vxBTFhaElYDq186sK69/YnvBZf6LwFJkFQDTo1SgrCPe3hoQwEMdWMIPoqdAfnMKte/gZP/OPbXp9H7CHb1OGBtVAf94BDnVaJvSfacbzB0e0ssSDLJdyntiNUCVZoih0vvvPKibjmohGoiNMDJ4hchUIHOYo4aiZePL4gdBolmNBmlcipZif+sO5rnD95IAIBVtrhjwe9RiU1epGPl4lGi80LawJdtmyhSjDRo42GSa/G96dWxX1OgshVyKPNUX4fCh/EQ4vNg9sf24RPdnQtia1rcYHleHy0/RT8QQ7qBKbcymOjl53dNdd28vBS6efIuWM9Ud8qfJAUm6mhN9H/IaHtA7Bc952yxIbZYmmsHHnPV3+AlfJj40GvVUr/XnLm4C7P/2L+BDx190woFQyeXr8He4+3xX3uL79rxECrCZUlFBYg+j8ktH0An5/t9nkuFB9VMF2fk+e5NrZ7EtpY0mmEyFIscVYoGOi1KihChte8thMPvLQtbBZYLBra3Bg3tKRLqIMg+iMktDlErA0lj697oRWFrbbZFdao+8X39+Otz45Kvzs9gYRitGLoQJ5hEA35OY+ctmPnoZZuj+d5Hi5vIKxvAkH0Z0hoc4ggKwhtpADJR7tEQ2w3aHf58erGQ3h363HsONiMz3bWdTk2kdlb+lAzcK6HjILFV00K+33fyfZuj/cHOQRZHiZD7I0wguhPUNZBDiFOf7UYNGFDFD09hA7kDbSPNTjwaRSBFekuTSsS0aONVhkmZ/jAAoweXIj9ocwEZw89EMT1RvbAJYj+Cnm0SfDhtlo0pmjkthxRaCPFMNKjbbF5wmKv8p+V0QK16OzpmkiMVvRo2R6EVlhj54dBT/0PxA06M3m0RJ6QU0J77NgxLFiwAJdccgkWLFiA48ePZ3tJXfD5Wby68RDuf3Fbys8thg7OnVCJ6y4eKZW1eiNitPc8s1WqHgMAl6dTiGNtLYmFAYlkHcTr0QKdXneBSROW6RANUYjJoyXyhZwS2mXLlmHhwoXYsGEDFi5ciPvuuy/bS+qCNzShtbuR24Egh18+uSXh8llxEKJWrcTsMwbhyllDAQibWJGICf+A0JxFJBghijdcMgprbjsHllDcN7HNsPhitABgDolmldXUo0crjbChzTAiT8gZoW1tbcXevXsxZ84cAMCcOXOwd+9etLXFn5uZCXw9bEwBQLvDiw6nH69+dCihcwdDgxDFXX6LUQMGQIezc7z3Nwe6dsvqkImu/FgAqCo3odiik7zHZAoW4vFo/3vuOPz00lEYZDX16NGKHxwUOiDyhZzZDKuvr0d5eTmUSuGPW6lUoqysDPX19SguLo7rHCUlpoTtWq3mhI53BjqLBwqLjFFjnt7QIS02L9xBHtWVli7HRLMvnruk2CA9XmDSwsfysFrN4HkeTz30cZfX2tx+XDy9GqebnfjuaCvkTB5TAZ1GhYpSE3CoBRazPsxmd9fPKoRr43i+x/fJajVj1DArXt94EP4gh8IiQ8wMB7snCJVSgdICHZQ9pI5lgkTvAbLfP2xn0n7OCG0qaG11xuV9iVitZjQ3J1g62miXfq493R7VKztdb5N+/s2Tm/HEXTPjst/ULLQxdDl90uNmgxoNzU40Nztgixgd09zsQJDlYHP4oFMxUEUEaO9fNB0OmwcOAKIj29DskM7d0/W7QyEJluXjf59YIbRyorYdYBiseOFr/GD6YFwk61lwuLYdFcUGKJWKhN//VJPMPUD2+77tdNjvTrSz706EqKysRGNjI9jQHyrLsmhqakJlZWVa7PE8H9dueiTyKq1YFVAeWXghEOy+fFaOmHWgknnJBSYNOkIC22rzdnmN3eUHD2GzS6cN/9yU9yooCG2GRYp1d4gx2kQQO4O5vEG02rxod/jwvxsPSWXEPM/jVLMTA0qp9JbIH3JGaEtKSjBmzBi88847AIB33nkHY8aMiTtskChr39uHx/++I+HXecOENnp+q/xxRYx0q2iIm2Fq2dfpQqMWtlDctSU0kluOmG9rMWqglwnrD6YPDpukMG10GYZUWqIOYoyFWqXApdMH4/9dPyXu1xh1gji7vIGwKrVbHv4UAHCs3oE2uw9ja9Lz/0oQuUhOhQ6WL1+OpUuX4umnn4bFYsHq1avTZkutVODLPfVYOHt4QmLojcejlT0eK681GiwbvhkGACaDGjaXHx5fEO2O8I0ujuclW3qtKiyMMeecmrBjTXo1fv/TqXGvReQnFwxP6Hi5R6uKuPbNu+qkzbqpsibiBNHfySmhHTZsGN54442M2BpZVYhPd9ahtsmJ6or4A+K+QHqEdtv+Jjy9fg+AztlZgJA2xfPA7Y9twrzzhoSvxc9Kwq/TKMOuQ5ul8S+iR+v2BrqEHtb9336cMdKKIrM27ubjBNEfyJnQQaYZXC6IktgXNV7kVVqeGKlecqFl4hTatz47Iv0s92iNsqR+X4CFUsHgqguGhdbCSuvRaZQYEspuKDJrpWm1mcYghQ6CUePT2w82o7wo/gbhBNEfyCmPNpNYC3VgGKCpvWvcszvCPdroMVp3Eh6tfGNOXjFllv3scAegUStQWqAP/e6XebQqFJm1uOGSURg7JHvxT4Pk0QZjDlWk0TREvpG3Hq1apURJgR6NcQhtIMjhtY8OweH2w+UNQhsaf+2OETo43dzpJScjtBZjZ6zVJKuesjn90KiUUrPsuhaX5FWLTbrP/95AlCUwUibVKBUK6DRKuLwBqQADAEYOKoDoZJcVkdAS+UXeCi0AVJYY0dzRs9Aeq7fjg69rsfNwCxzuAIotWqiUDLxRhNYfYHHoVOd8LYWi57c4EORgj5F2pZelbNmcPqhVCiEHVcGgtskJr48FA0jinwsYdWq4vUH4Q0Kr0ygxf9YwIPRZUl5MoQMiv8jb0AEAFFm0aIyI0W7b34SaSrP09RwA2hxC/mpzhxcOlx9mvRp2lz8sjCDS7vBJzWGA+DzaU81OsByP70+twkVTB4U9V2zunBJrc/lh1KuhUipQWWLE6RYXyosM0EYZyphNCkwatNm9UojgT3eeB7VKIeoshQ6IvCOvPdoCkxYOT6cnyfM8nl6/B8vXfh12XLtdSEl654vjOFDbAbNBA41aGVVoIzfI4qlUOx4abPj9qYO6TJI16NS49wYhLcvm8kslvxUlBjS2ueH1B8O83lygpsKM/Sc70NTuAYPOLIoZE4TiE/mHGEHkA3kutBp4fKy0O+4P9Rpw+4KobRLKYXmeR5s9PH9VqWSgVSul4+WIG2Q/v3wsiszauCrDxIKE4oLoE2HFDSYA0IaEtrxIj+YOL1zdbDpli2EDCgAAW76th1qlkLztGy4dhcd+cW5CPXEJoj+Q13d8gVH4Wi62GfTKPNQVL3yNfcfbcPPqT7DrSPgMrDaHDxq1IqpH++0RoalLZYkRk4aXhlVHxcLjY6HVKGOmZMk9VnUoFlteZAAXKmfNpfgsAEwbUyb9LBdVlVIhlQITRD6R30JrEoVWKGOVt0BkOR7bQv1kWyJ6DJwxwhryaMNF9PBpG97/6iQAQKdVQq1USGW13eH1B8PKZyMxaDufE/vJFpqFzISWDm/OebQqpUKqTEukMo4g+it5LrSCWEkebcRsrk+2n5Z+FtOnRlUV4uIzq6BVK+GLCB3IG3TrNSpo1Iqo4YVIPH622wYuapVSKmLQhLxXnbqzKbcmxzxaACgNhUGcnp779xJEfyevhbYwwqPdf0KY3vqrqydLQiEyoMQIQIjPKhgGmigerdx304c8Wpbjpc5VsRA2tLoXS9GrFb+Ky0tsE5makCnETb14pjMQRH8n9/5CM4glJLR2tx/+AIvXPj4MANCplWFFAwBw1rgKjK0pwtUXjgAAaKPEaOUesUqpgD60iRWrgkx6na97jxbojNNqogltDnq0IwYVZHsJBJEz5FZeUIYx6lRQKhg43IEwkdRqlF3inlVlJlw4pTPHNVqMVp7axTCMVD7rcPu7HUTo8QdhNnSf8iRmHoiiKt8Ay0WhVSkVWLloelh1GEHkK3kttAzDwGxQw+4OLz7QaZRhHubwQQVdPDRNlBht5LRaUVyjDVeMfF1PubBiK0exO5ZOnduhAwAYWGrM9hIIIifIzb/QDGIxaOB0ByKEViV5tBXFBvz22jO6VF6JMVpeFoMUu3atuPlMAJ19CnoUWn/PubDiwEMx2V8+ZDHX0rsIgggn74XWbFCjtskR5o1q1Z2hA702enmrVi2UlH74dS12h3JnPaEqrUFWYUikKdRz1emOLbQcx8Pr79mjdYXEWtykUzCMlDqlSWCyLUEQmSfv/0JHVhWi1e7D1r0N0mNqlUISvlibVOLjr318GH98YxdsTh8a2zxh2QOSR+uNLbQnGh1gOR4Drd1/zXaJHm2Uzly5GKMlCKKTvBdacYOrvkVoLnPfz4S+AqJHG6tc1KgLF+C7n/wc3x5thV4mzFq1Elq1Ep/uOB2z58GBk0KnrzGDi7pd508vHY0SixYWWdtE0dOm0AFB5DZ5vRkGCGlTSgUjVX+JoiX2hy2LMQ0g1igWeXYBwzCYMbESH31zCh1OX9iwRJEOpw9ajbLH0tQZEysxY2L4RGCx6CpXN8MIghDI+79QMfMgUmjFDayqULw1EqM++mdUkSVcMEdVFQLo3MyKxOtnwzIIEiGyWowgiNwk74UWQNj0WLEQ4LKza/CD6YNx9viKqK8xxvBoIze1DLLx29GIJ+MgFueG2g7mUCtagiCikPehA0DIPBARPdoCowZXdTNqOzJGKxLZgUuaoRVj7I3PzyY9sfaqC4ZhcLkJE4aWJPV6giAyAwktOj1apYIJm0DbHYYYQlto0kQcJ4h4rNCBL9C70IHo1RIEkbtQ6ACdk2YT2b1XRpkFNq6mCJecOTjsMUMolLDhq5NhxQ0iXj8LbQ99DgiC6NuQ0AIwhxrIxPJSY3H9xSPx33PHSb//8OyaLh6xKLSnml2oC6WQ7TjYjE276gCENsNyrJ8sQRCphVwpdMZoY004iMUFZwySym6B6HFbhazxtRinfeIf3wIACgv08AWSj9ESBNE3II8WgFkveLTJ7N7Ly19jdegaUmkB0LUU94+v7UC7w5d0jJYgiL5BTgjt0qVLMXPmTMydOxdz587FM888k1H7okebzMhupUIh9RyIlfJ16zwhvOCI0VyGjWNSLkEQfZecCR38/Oc/x3XXXZcV22KMNNl8VLVKAYblYjZ3ET1mpyeAFpuny/MdTl+XxwiC6D/kjNBmk/IiAyxGDRbMjp032x0alQJadfQuX4AQXlCrFHB6Arjnma3S45NHWDF8oAVTR5dFfR1BEP2DnBHadevW4e9//zuqqqrwq1/9CsOGDUv4HCUl0ctlu8NqNQMAXlnxg4RfK6LTqqDVqKRzRcNi1CAQESJQKhn89PLxSdtNBd2tOR/s58Ia8tl+vlx7RoT2iiuuQF1dXdTnvvjiC9x9992wWq1QKBRYv349Fi1ahI0bN0KpTGyTqLXVGbNLVjSsVjOamx0J2YiGUsFAp1Z0ey6dWol2mxcWgxr20KbY1DHlKbGfLKm6/r5qPxfWkM/2+9u1dyfaGRHaf/7zn90+X15eLv08b948PPjgg2hoaMDAgQPTvbSUUGLRSRN1YyGMvmGhVCowY2Il5s0YgpFDS9HS4szQP2RkqAAAFXVJREFUKgmCyBY5ETpobGyUxHbz5s1QKBRh4pvr3D5/AhQ9bKSJU3PFbl3FFl1SWQ4EQfQ9ckJof/Ob36C1tRUMw8BkMuGZZ56BSpUTS4uLeEp3tWol3A4ffH4WOi3lzRJEPpETavbCCy9kewlpR6tRwukNgON5mohAEHlGThQs5AMatRJ2lx9A7DlkBEH0T0hoM4RWrUSQ5aWfCYLIH0hoM4S8Qxd16yKI/IKENkPI53qR0BJEfkFCmyHk4QJqi0gQ+QUJbYbQyhrO0GYYQeQXJLQZgjxagshfSGgzhNyLpUbfBJFfkNBmCPk8MtoMI4j8goQ2Q8iFVq2it50g8gn6i88Q8jE31EyGIPILEtoMIY4dJwgi/yChzRCx5okRBNH/ob/+DEHhAoLIX0hoCYIg0gwFDjPIkEozCozdj7whCKL/QUKbQX7/02nZXgJBEFmAQgcEQRBphoSWIAgizZDQEgRBpBkSWoIgiDRDQksQBJFmSGgJgiDSDAktQRBEmiGhJQiCSDMktARBEGmGhJYgCCLN9KsSXIUi8Q5ZybwmlZD97Hc1y/Ya8tl+vlw7w/M8nxFLBEEQeQqFDgiCINIMCS1BEESaIaElCIJIMyS0BEEQaYaEliAIIs2Q0BIEQaQZElqCIIg0Q0JLEASRZkhoCYIg0gwJbT+moaEh20sgiKyRS/d/vxdajuOyZtvhcGTFrtvtxoMPPoibbroJ9fX1WVkDABw5cgRvvPFG1uwDgMvlyqp9ILv3YDbt5/v9L6dfCq3T6cRjjz0Gm80GhUKR8RvN5XJh1apVuOuuu7B27Vrs27cPAJCJthL/+te/cPHFF6OgoACvvvoqKisr024zErfbjVWrVmHBggX44IMP4Ha7M74Gl8uF+++/H0uWLME//vEPHD16FEDmRCfb92A27ef7/R+Nfie0W7ZswY9+9COsXbsWK1asAAAwTOY6BL377ru46qqroFarceutt2Lnzp3YuHFjxtZht9sRCARw2223oaCgAIcPH0ZHR0fa7Yq89dZbuOqqq1BYWIh7770XdrsdBoMhY/YBoLGxEXfeeSf0ej2uvvpq7N27F8uWLQMAKBTpv+WzfQ9m036+3/+x6FdtEgGgsLAQt99+O2bOnIkrrrgCu3btwqRJk8CyLJRKZdrtl5SUYPXq1ZgwYQIAYP369RgzZoz0PM/zKb3hamtrsXPnTpx//vkwmUy4/vrr8e6772LZsmVgWRYnT56EUqnEjBkzsGDBAphMppTZjqS5uRl+vx9//OMfMWLECPh8PvzlL3/Bzp07MXny5LTZjaS+vh4ulwu/+tWvAAAGgwGvv/46nnrqKdx+++3gOC4tgiv+3xYVFWX1Hszm30A+3//doVy+fPnyrFhOEbW1tdi0aRMqKyuh0WhQXl6O6upqFBYWwmaz4dVXX8VVV12VNk9GtD9gwABoNBpUVVWhvLwcLS0tuOWWW7Bv3z60t7dj9+7dGDt2LAwGQ8putj/+8Y94+OGH0drais8//xzNzc2YNGkSSktL8dhjj+GHP/whVqxYAYVCgd27d8Pv92P06NEpuOpOamtr8dlnn2HAgAEoKirChAkTUFJSAgA4ffo0du/ejbPOOgvl5eUptRu5BvEe0Gq1aG9vx3fffQeTyYTq6mqcPn0aDocD77//Pn70ox+l/I+tra0Ner0egOC1lZWVYejQoSgoKMjoPZiNv4F8v//jpU8LbeQb3dLSgokTJ0KtVoNhGIwbNw4vvfQS9Ho9xo4dmzH7AKDVauH3+7FmzRqMGzcOW7Zswa5duzBz5syU3GQtLS1488038eKLL+KKK66AwWDAww8/jLPPPhvTpk3D5ZdfjlmzZgEARo0ahTfffBOTJk3CsGHDem1bRLz+trY2fPHFF2hqasKkSZMQCASgVCpRWFiIv/71rygvL8eECRPAcVzKvz7K/w+2bNkCh8OBmTNn4tixY1i7di2OHDmC1157DTfffDOcTieMRiOGDx+eEtvBYBCrVq3C4sWLMXHiRNTU1IBlWSgUCqhUwpfFbN2DmfgbyPf7PyH4PkpzczN/66238m63m+d5nt+0aRN/3nnn8fv27eN5nucDgQDP8zz/1ltv8RdffDHP8zz/ySef8KdOncqIfZZlw45/4okn+D//+c8psc3zPN/a2sqfddZZfENDg/TYQw89xC9YsKDLsd9++y1/7bXX8t9++23K7Pd0/T6fj+d54bqXLVuWMrvdreGzzz7jZ8yYwR88eJDneZ7funUr/+abb/Lt7e08z/P8T37yE3779u0ps//yyy/z//Vf/8U/+uij/OWXXx7zuGzdg+n8G8j3+z9R+uxmmEKhwI4dO2C32wEA5513HubMmQPRQRc9ivnz5wMAJk2ahJdeekl6PN325V/T9u7di88//xwjRoxI2A4fZaeW53loNBpcdNFF0kYDANxxxx3o6OjAli1bAADHjx/H3XffjRUrVuCaa67B+PHjE7Yfi56uX6PRdHlNqne+I9cwc+ZMzJkzB/feey8A4KyzzsKVV16JwsJC7NixA0ajEWVlZSmzP3fuXPz2t7/FXXfdBYZhsG7dOgAAy7Jhx2XrHkzn30Cm7v9o5ML9nyg5L7SJCM0vfvGLsDfa7XZj8eLF4HkeDz74IJ5//vmEY4W9sX/o0CEsXrwYy5cvx3XXXYcLLrggIdtA+E6tuBaGYaDRaDBixAjs379fSl1SqVQ499xzcerUKQBAXV0dJk+ejNdffx2XXXZZwrblNiMfi+f6AaCmpgbbtm0DkPyOf6IfNjabTVpDS0sLbr75ZqxcuRLz58/HwIEDU2IfgBQDZhgGd955J55//nm4XC5pw4llWXi93l7fg7HWlKm/gWRtp+L+F+3JyeT9nypyPkYbKTQMw0iP1dfX4+DBg6ipqUFRURF4nsfJkyehUqkwYcIEqNVq+Hw+PPDAA0l/miZjX61WY/z48Th16hTUajVWrlyJkSNHJmT3rbfewkMPPYRDhw4hGAyipqYGDMOAZVlwHAe1Wg2tVotDhw5h9+7dmDVrFpRKJV5//XWcc845qK6uxqBBg3q929+b9x8ARo4ciYULF2ZtDQaDAcFgEH/4wx8S/j/ozn4kQ4cOxVdffYUdO3Zg9uzZ4DgOSqUSKpUKXq8Xq1at6pVHF2k3k38DidpOxf2/fv16HDhwAKNHjw7LEuE4DizLZuz+TxU5K7S9EZoZM2Zg8ODBAJD0LmMqhK6iokLaHEiE999/Hy+88AIWL14Mh8OBt956C3q9HiNGjIBCoYBCocCBAwdgNBoxYsQIvPzyy9i+fTvWrl0LhmFw5ZVXwmw292rTIVXvf29I1RqS3QTqzr5c7EWmTZuG1atXw+l04vnnn8fQoUNRVlYWlt6UCL0Rm97+DaRC6JK5/+12O+666y68+eab+Oijj3DNNddAr9dLG6kMw2Tk/k81OSm0vRWa+fPnw2w2Z82++B+dLM8//zzOP/98/PCHP8TkyZOh1Wrx0EMP4aabbgLLsli1ahWefPJJnHPOOZg4cSIuuugiFBQUYNiwYfjNb37TK9upuP7evv+5sIae7DMMg/3798PtdqOgoACAEBN888030d7ejjvvvBNnnHFGUrZTITbJXn8qhS4ZtFotGhsb8eCDD6KhoQGfffYZvv/970tetcfjwZo1a/D444+n7f5PC2nfbkuCpUuX8i+//DLP8zwfDAb5f//73/y5554r/b5y5Ur+ggsu4L/++mue53m+paWF//TTT/l//vOffdL+hx9+yB8+fJh3OBw8z/P8c889x//6178OO+aaa67hn3jiCb61tZVfu3Yt7/f7k728Hsn2+58La4jH/oUXXijZb25u5ufNm8e/8sorKbH/3HPP8R0dHfzSpUule0HcyXe73fyqVavSdv2Zth15/zudTp7nef7w4cP82Wefze/evVs6dt++ffyLL76Y1vs/HeSE0GZbaLJl/8MPP+SvuOIK/uabb+ZvueUW/qGHHuKDwSC/adMmfsmSJfzWrVulYz///HP++uuv510ul/RYMBjs9RrEdWRb6LO9ht7Y5ziO5/muKU29sZ9JscmW7Wj3f+R516xZEzVli+dTd/9ngqyGDjZu3IglS5bgyJEj+OKLL3D48GGcffbZcLvdOHDgACwWCwYNGgQAqKysxL/+9S/MmzcPZ555JpRKpZQc3pfs86GvQLt378aTTz6J22+/HXfeeSfMZjO2b9+O0aNHo7KyEkePHsXBgwcxbdo0qNVqnDp1CvX19Zg9e7Zks7eVPtl+/3NhDam0n0xMMJr96dOnQ6fTAQCKi4vR0dGB1157DT/+8Y8BAKWlpZg0aVKvrz8btru7/3fs2IEhQ4agtLRUOn706NF45ZVXUF1djaKiImzZsgVDhw4Fz/MZ6VuRKjK+Uj6UqrF7926sW7cOixcvxnPPPYeFCxeisbERJ0+exKhRo1BRUYGNGzfC4/EAEG7igQMHQqlUguM48DyfVN12Nu37/X7pj3Hw4MH4/e9/L1WvTJw4EXv37oVKpUJZWRlmzJgBm82GFStWwO/347333gPHcdBoNFKMMBmy/f7nwhpy2X5TUxOOHDkSdvyNN96Ijo4ObN68GXa7HR999JF0nkTtZ9N2PPd/ZHl0cXExbrjhBtxyyy2YN28evF4vgMw26UkFGRXabAtNNu2/8MILuPrqq7F69Wq8/fbbKCwslNJtWJaFw+GA1WqVksmnT5+Oe+65B263Gz/72c8QDAaxfPnyXt1g2X7/c2ENfcF+usQmm7bjuf9LS0uhVqvD8mbXr1+PZ555Btdeey3efvvtrOfDJkvGune98MIL+Pe//43p06dj7NixuPzyy6XdwVhCM3ToUNx///342c9+hurq6l4JTTbtr1+/Hh988AGWLVuGkydPYvXq1SgqKsKMGTMQCASgVquxf/9+GI1GqX/m/2/v/kKa6sM4gH81tXBKgSKUaIGsXWSQITMLNJtJSq1yRROW/cMyvHOb1MUgtCAJWleGNrEULOqi7I8sbyxMwW7yooy6iHI5RRiW2xRx9rwXsoO+b72Q7vg7R5/PlavTvr/nx6/Hw/kbHs+NGzcwPT295DOpoudfCWNQQ/78ZhPOmd9sqqurodFoVJX9N+s/fDNFMBiUPjc1NS36EjnFWI4DwY8fP6aysjIaGBiQzt729PQQEUkHv91uN124cEH6NxMTE0Q0d898+Gc15YdPLBAR1dTU0NOnT6XPbW1ttH///gUnT65fv04dHR3k9/vJbrdTc3PzX2f+iej5V8IY1JgfPinV19dHg4ODqspe6vp3uVx/nalksjVa0Y1GVP7MzAzdvHmTLBYLOZ1O+vTpE7W1tVFVVdWC7UpKSqTFFAgE6NSpU3TixAk6evQoNTU1LSp7PtHzr4QxqD1/Kc1GVLZS1r/SRPwYbSgUgtPpxMWLF3Hr1i18/vwZ27dvx8uXL6VtLBYLYmNjpYdwBINBfPz4Ee3t7SgvL4dWq8XZs2dVl//u3TuYTCYEAgFYrVYQEerr65GRkYFQKIS3b99K29psNjx58gQ098sOQ0ND0Gq1aGlpQUVFxaJqF12/UsawUvLPnTunqmwlrH+limijFT3RovNjY2NRVlYGh8OBHTt2IC8vDwkJCUhISMC2bdvQ3t4ubZuUlCQ9uzU6Ohr3799HXV2ddJeRGutXwhhWc77o2kWvfyWL6Mmw8ESbzWYAcwfZv379umCi9Xo9gN9P9FKfKiQ6X6fTSdf4RUVFQaPRYHh4GJmZmUhMTMSVK1dQW1uLM2fOwOVyYf369YiLi0NcXFxE3qslun4ljGE154uuXfT6V7KI7tHqdDoYjUbp8oz5E33o0CH8+PEDtbW18Hg8cLlc0uPO4uPjI/KfXHR+bGws4uPjpTO279+/lx5EsmXLFly7dg1RUVGw2+1IT09HXV3dkjPnE12/EsawmvNF1y56/StZRBut6IkWnR8WfvDzhw8fpD0It9uNyclJOBwOtLS0wGazRTxXCfWLHsNqzhdde5io9a9kslxHG37b5r8nOiMjAw6HA1NTU9LL7FZi/po1a0BE8Pl8mJqagtVqhc/nw+XLlwFA1mxAfP1KGMNqzhddu+j1r0Sy3Bn2u4l+8OCB9CoTuSdadD4AfPnyBV1dXXjx4gV27dqFu3fvQqfTyZ4LKKN+0WNYzfmiawfErn9FkuWiMZp78o9Op6Pjx4/Tw4cP5YpRbL7f76fGxkbpJYXLTXT9ShjDas4XXbvo9a80st6wIHKiReeLpoT6RY9hNeeLrp0tFEX0hzfPMcYYiwj1PNCRMcZUihstY4zJjBstY4zJjBstY4zJjBstY4zJjBstU7V9+/ahr69P9DAY+1/caBljTGbcaJlq2e12eL1eVFZWIisrC3fu3MHAwADMZjOys7NhNBrR398vbX/y5Ek4nU6YzWZkZWWhsrIS4+PjsFqt2LlzJ0wmE75//y5tr9Pp0NraCoPBgJycHNTX10u3sf769QsNDQ0oKChAbm4uampq4Pf7l30OmEqIvmOCsaUoKCig3t5eIiIaHR0lvV5Pr169otnZWXrz5g3p9Xry+XxERGSxWKiwsJC+fftGExMTVFxcTEVFRdTb20szMzNkt9vp0qVL0ndv3bqVLBYLjY+P0/DwMBUVFUm3sz569IgKCwtpaGiIAoEAVVVVkc1mW/4JYKrAe7Rsxejo6EBeXh7y8/MRHR2NPXv2IDMzE69fv5a2KS0tRXp6OhITE5GXl4e0tDTs3r0bMTExOHDgAAYHBxd8Z0VFBTZs2IBNmzahvLwcz58/BwA8e/YMp0+fRlpaGjQaDaqrq9HZ2YlQKLSsNTN1WLbXjTMmN6/XC7fbje7ubunPQqEQcnJypM/JycnSz2vXrl3wed26dZicnFzwneHXvwNAamoqxsbGAABjY2NITU1d8HehUAg+ny9iD1FnKwc3WrZibNy4EYcPH8bVq1cj9p0jIyPQarUA5hp5SkoKACAlJQXDw8PSdl6vFzExMUhKSopYNls5+NABU7Xk5GR4PB4AgNFoRHd3N3p6ejA7O4vp6Wn09/djdHR00d/f3NyMnz9/YmRkBK2trSgpKQEAHDx4EPfu3YPH40EwGITT6URxcTFiYnjfhf0XN1qmaufPn8ft27eRnZ2Nzs5ONDQ0oLGxEbm5ucjPz0dzc7N0pcBiGAwGlJaW4siRI9i7dy+OHTsGADCZTDAajbBYLDAYDIiLi4PD4YhUWWyF4cckMvYHOp0OXV1d2Lx5s+ihMJXjPVrGGJMZN1rGGJMZHzpgjDGZ8R4tY4zJjBstY4zJjBstY4zJjBstY4zJjBstY4zJjBstY4zJ7B/V77QXLh3NwQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wM9AXJjYmCU2"
      },
      "source": [
        "# **Introdução ao Mercado Financeiro**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IYhC3wpqlJq",
        "outputId": "f40324e2-0af9-44df-a0ac-ee2ddd14defd"
      },
      "source": [
        "# Instalando o yahoo finance\n",
        "!pip install yfinance --upgrade --no-cache-dir"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.69-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 6.0 MB/s \n",
            "\u001b[?25hCollecting lxml>=4.5.1\n",
            "  Downloading lxml-4.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 9.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.10)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.7.1 requests-2.27.1 yfinance-0.1.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRJa8hFEeOhK"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFGRlgU2a8vb"
      },
      "source": [
        "import yfinance as yf #Instalando e importando a API no código"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbIuAe_dfogu"
      },
      "source": [
        "import requests"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "WAm8R5X8bALy",
        "outputId": "e4badcfe-6705-4386-ad64-0a5a96bcbe4f"
      },
      "source": [
        "url = \"https://investnews.com.br/financas/veja-a-lista-completa-dos-bdrs-disponiveis-para-pessoas-fisicas-na-b3/\"\n",
        "r = requests.get(url)\n",
        "html = r.text\n",
        "df_nomes_tickers = pd.read_html(html, header=0)[0]\n",
        "df_nomes_tickers.head(10)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6c7a36b7-1b27-4f6b-bbf6-ee75a097db73\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>EMPRESA</th>\n",
              "      <th>CÓDIGO</th>\n",
              "      <th>SETOR</th>\n",
              "      <th>PAÍS DE ORIGEM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3M</td>\n",
              "      <td>MMMC34</td>\n",
              "      <td>Indústria diversificada</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AB INBEV</td>\n",
              "      <td>ABUD34</td>\n",
              "      <td>Bebidas</td>\n",
              "      <td>Bélgica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABB LTD</td>\n",
              "      <td>A1BB34</td>\n",
              "      <td>Industrial</td>\n",
              "      <td>Switzerland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABBOTT</td>\n",
              "      <td>ABTT34</td>\n",
              "      <td>Farmacêuticos</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ABBVIE</td>\n",
              "      <td>ABBV34</td>\n",
              "      <td>Medicamentos e Outros Produtos</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ABIOMED INC</td>\n",
              "      <td>A1BM34</td>\n",
              "      <td>Saúde</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ACTIVISION</td>\n",
              "      <td>ATVI34</td>\n",
              "      <td>Tecnologia</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ADOBE INC</td>\n",
              "      <td>ADBE34</td>\n",
              "      <td>Tecnologia</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ADVANCE AUTO PARTS INC</td>\n",
              "      <td>A1AP34</td>\n",
              "      <td>Peças Automotivas</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ADVANCED MIC</td>\n",
              "      <td>A1MD34</td>\n",
              "      <td>Tecnologia</td>\n",
              "      <td>EUA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c7a36b7-1b27-4f6b-bbf6-ee75a097db73')\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c7a36b7-1b27-4f6b-bbf6-ee75a097db73 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c7a36b7-1b27-4f6b-bbf6-ee75a097db73');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  EMPRESA  ... PAÍS DE ORIGEM\n",
              "0                      3M  ...            EUA\n",
              "1                AB INBEV  ...        Bélgica\n",
              "2                 ABB LTD  ...    Switzerland\n",
              "3                  ABBOTT  ...            EUA\n",
              "4                  ABBVIE  ...            EUA\n",
              "5             ABIOMED INC  ...            EUA\n",
              "6              ACTIVISION  ...            EUA\n",
              "7               ADOBE INC  ...            EUA\n",
              "8  ADVANCE AUTO PARTS INC  ...            EUA\n",
              "9            ADVANCED MIC  ...            EUA\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-Q92qaJgY8i",
        "outputId": "b2b08f20-9245-4227-d928-bdb6ebdf766a"
      },
      "source": [
        "codigos = df_nomes_tickers['CÓDIGO']\n",
        "codigo_especifico = codigos[1]\n",
        "print(codigo_especifico)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ABUD34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDpHwLa1jKku",
        "outputId": "7d46b05d-e023-4f1f-9b64-ec3199f93f02"
      },
      "source": [
        "codigo_especifico\n",
        "codigo = [str(codigo_especifico+'.SA')]\n",
        "codigo"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ABUD34.SA']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzpzJqFEmRks",
        "outputId": "187ffdd7-09a0-4081-bcc9-2339ef0dd504"
      },
      "source": [
        "df = yf.download(codigo[0], \n",
        "                 start='2021-08-01', \n",
        "                 end='2021-10-01',\n",
        "                 group_by=\"ticker\"); # \";\" serve para não exibir os dados\n",
        "             "
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "IbaM6lOSm0TR",
        "outputId": "b4ddd37f-7ebd-40f3-ccc1-007cb6ca7439"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-74c8a314-557c-454e-bb77-dc7d640b5950\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-08-02</th>\n",
              "      <td>54.500000</td>\n",
              "      <td>54.500000</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-03</th>\n",
              "      <td>53.950001</td>\n",
              "      <td>54.599998</td>\n",
              "      <td>53.939999</td>\n",
              "      <td>54.250000</td>\n",
              "      <td>54.250000</td>\n",
              "      <td>1289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-04</th>\n",
              "      <td>54.099998</td>\n",
              "      <td>54.099998</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-05</th>\n",
              "      <td>53.150002</td>\n",
              "      <td>53.750000</td>\n",
              "      <td>52.900002</td>\n",
              "      <td>53.650002</td>\n",
              "      <td>53.650002</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-06</th>\n",
              "      <td>54.910000</td>\n",
              "      <td>54.910000</td>\n",
              "      <td>53.700001</td>\n",
              "      <td>53.849998</td>\n",
              "      <td>53.849998</td>\n",
              "      <td>567</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-74c8a314-557c-454e-bb77-dc7d640b5950 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-74c8a314-557c-454e-bb77-dc7d640b5950');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close  Volume\n",
              "Date                                                                     \n",
              "2021-08-02  54.500000  54.500000  53.950001  53.950001  53.950001     165\n",
              "2021-08-03  53.950001  54.599998  53.939999  54.250000  54.250000    1289\n",
              "2021-08-04  54.099998  54.099998  53.290001  53.290001  53.290001     223\n",
              "2021-08-05  53.150002  53.750000  52.900002  53.650002  53.650002     110\n",
              "2021-08-06  54.910000  54.910000  53.700001  53.849998  53.849998     567"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "CSq77vifmXjE",
        "outputId": "f5faa3bd-943d-4d4b-ce83-b91335d094a3"
      },
      "source": [
        "df['Close'].plot(title = str(codigo)+ \" valor de fechamento\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe640b7f410>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJ2u9Dn5pdfW"
      },
      "source": [
        "**Análise de Várias Ações**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJAPqF2jpiB1",
        "outputId": "78c67278-02ed-468e-f406-36b90f0a205c"
      },
      "source": [
        "tickers = ['MMMC34.SA', 'ABUD34.SA', 'A1BB34.SA', 'ABTT34.SA', 'ABBV34.SA']\n",
        "\n",
        "df = yf.download(tickers, \n",
        "                 start='2021-08-01', \n",
        "                 end='2021-10-01',\n",
        "                 group_by=\"ticker\"); "
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  5 of 5 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "VOTEnJ4lqtMs",
        "outputId": "df8b92fc-4574-4960-b611-2313c4e4dc0d"
      },
      "source": [
        "df.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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"6\" halign=\"left\">ABTT34.SA</th>\n",
              "      <th colspan=\"6\" halign=\"left\">MMMC34.SA</th>\n",
              "      <th colspan=\"6\" halign=\"left\">ABUD34.SA</th>\n",
              "      <th colspan=\"6\" halign=\"left\">A1BB34.SA</th>\n",
              "      <th colspan=\"6\" halign=\"left\">ABBV34.SA</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-08-02</th>\n",
              "      <td>155.470001</td>\n",
              "      <td>156.449997</td>\n",
              "      <td>155.350006</td>\n",
              "      <td>156.449997</td>\n",
              "      <td>155.882401</td>\n",
              "      <td>19</td>\n",
              "      <td>256.130005</td>\n",
              "      <td>256.130005</td>\n",
              "      <td>254.529999</td>\n",
              "      <td>255.839996</td>\n",
              "      <td>253.998306</td>\n",
              "      <td>36400</td>\n",
              "      <td>54.500000</td>\n",
              "      <td>54.500000</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>165</td>\n",
              "      <td>46.799999</td>\n",
              "      <td>46.799999</td>\n",
              "      <td>46.799999</td>\n",
              "      <td>46.799999</td>\n",
              "      <td>46.799999</td>\n",
              "      <td>3</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>599.599976</td>\n",
              "      <td>595.0</td>\n",
              "      <td>599.599976</td>\n",
              "      <td>592.868835</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-03</th>\n",
              "      <td>160.490005</td>\n",
              "      <td>161.229996</td>\n",
              "      <td>158.080002</td>\n",
              "      <td>158.080002</td>\n",
              "      <td>157.506485</td>\n",
              "      <td>595</td>\n",
              "      <td>260.269989</td>\n",
              "      <td>264.700012</td>\n",
              "      <td>260.269989</td>\n",
              "      <td>261.299988</td>\n",
              "      <td>259.419006</td>\n",
              "      <td>1355</td>\n",
              "      <td>53.950001</td>\n",
              "      <td>54.599998</td>\n",
              "      <td>53.939999</td>\n",
              "      <td>54.250000</td>\n",
              "      <td>54.250000</td>\n",
              "      <td>1289</td>\n",
              "      <td>48.509998</td>\n",
              "      <td>48.509998</td>\n",
              "      <td>47.950001</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>602.500000</td>\n",
              "      <td>604.000000</td>\n",
              "      <td>602.5</td>\n",
              "      <td>602.760010</td>\n",
              "      <td>595.993408</td>\n",
              "      <td>154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-04</th>\n",
              "      <td>159.020004</td>\n",
              "      <td>159.020004</td>\n",
              "      <td>158.229996</td>\n",
              "      <td>158.229996</td>\n",
              "      <td>157.655930</td>\n",
              "      <td>408</td>\n",
              "      <td>261.299988</td>\n",
              "      <td>261.299988</td>\n",
              "      <td>256.200012</td>\n",
              "      <td>256.200012</td>\n",
              "      <td>254.355743</td>\n",
              "      <td>1703</td>\n",
              "      <td>54.099998</td>\n",
              "      <td>54.099998</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>53.290001</td>\n",
              "      <td>223</td>\n",
              "      <td>48.840000</td>\n",
              "      <td>48.840000</td>\n",
              "      <td>48.389999</td>\n",
              "      <td>48.419998</td>\n",
              "      <td>48.419998</td>\n",
              "      <td>19</td>\n",
              "      <td>609.119995</td>\n",
              "      <td>609.119995</td>\n",
              "      <td>598.0</td>\n",
              "      <td>598.000000</td>\n",
              "      <td>591.286804</td>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-05</th>\n",
              "      <td>155.520004</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>155.520004</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>155.434036</td>\n",
              "      <td>16</td>\n",
              "      <td>256.059998</td>\n",
              "      <td>258.019989</td>\n",
              "      <td>256.059998</td>\n",
              "      <td>257.049988</td>\n",
              "      <td>255.199600</td>\n",
              "      <td>90</td>\n",
              "      <td>53.150002</td>\n",
              "      <td>53.750000</td>\n",
              "      <td>52.900002</td>\n",
              "      <td>53.650002</td>\n",
              "      <td>53.650002</td>\n",
              "      <td>110</td>\n",
              "      <td>47.849998</td>\n",
              "      <td>48.320000</td>\n",
              "      <td>47.849998</td>\n",
              "      <td>48.320000</td>\n",
              "      <td>48.320000</td>\n",
              "      <td>53</td>\n",
              "      <td>595.650024</td>\n",
              "      <td>595.650024</td>\n",
              "      <td>590.0</td>\n",
              "      <td>591.000000</td>\n",
              "      <td>584.365417</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-06</th>\n",
              "      <td>156.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>159.660004</td>\n",
              "      <td>159.080750</td>\n",
              "      <td>185</td>\n",
              "      <td>260.130005</td>\n",
              "      <td>261.000000</td>\n",
              "      <td>258.200012</td>\n",
              "      <td>259.799988</td>\n",
              "      <td>257.929810</td>\n",
              "      <td>47</td>\n",
              "      <td>54.910000</td>\n",
              "      <td>54.910000</td>\n",
              "      <td>53.700001</td>\n",
              "      <td>53.849998</td>\n",
              "      <td>53.849998</td>\n",
              "      <td>567</td>\n",
              "      <td>48.599998</td>\n",
              "      <td>48.599998</td>\n",
              "      <td>48.099998</td>\n",
              "      <td>48.099998</td>\n",
              "      <td>48.099998</td>\n",
              "      <td>62</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>603.0</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>596.230713</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ABTT34.SA                          ...   ABBV34.SA                   \n",
              "                  Open        High         Low  ...       Close   Adj Close Volume\n",
              "Date                                            ...                               \n",
              "2021-08-02  155.470001  156.449997  155.350006  ...  599.599976  592.868835     20\n",
              "2021-08-03  160.490005  161.229996  158.080002  ...  602.760010  595.993408    154\n",
              "2021-08-04  159.020004  159.020004  158.229996  ...  598.000000  591.286804    311\n",
              "2021-08-05  155.520004  156.000000  155.520004  ...  591.000000  584.365417      3\n",
              "2021-08-06  156.000000  160.000000  156.000000  ...  603.000000  596.230713     11\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS6sRCLWu8Cz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "abaceb26-451a-43b2-e2ce-857d81363b89"
      },
      "source": [
        "for acao in tickers:\n",
        "  serie = df[acao, 'Close']\n",
        "  serie.plot()\n",
        "\n",
        "plt.title(\"Cotação x tempo\", fontsize = 25)\n",
        "# Legendas\n",
        "#plt.legend(loc='lower left')\n",
        "#plt.legend(loc=2)\n",
        "plt.legend(loc='best')\n",
        "plt.figure();"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}