visualizacao_de_dados.ipynb

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  "metadata": {
    "colab": {
      "name": "visualizacao_de_dados.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8ThuCTjEjuG",
        "colab_type": "text"
      },
      "source": [
        "# Notebook destinado a segunda parte do PP1 da disciplina de RNA 2020.1 - Colaboradores Jakson Protázio, Fabrizio Honda e Dayvson Silva."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkFE5yTuNiuZ",
        "colab_type": "text"
      },
      "source": [
        "## Importando as bibliotecas necessárias "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQrxap_dHVT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.style.use('grayscale')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WK5yHD74JW4b",
        "colab_type": "text"
      },
      "source": [
        "## Importando o dataset limpo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UL4Vu8BPDeEK",
        "colab_type": "text"
      },
      "source": [
        "> Formas alternativas de leitura do dataset do github"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IoqEcVSCIiU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#pd.read_csv('https://raw.githubusercontent.com/userddssilva/Analise-COVID19-PP1-RNA2020.1/master/dataset/dataset_limpo_covid19_manaus.csv?token=AMHWGQNWN4HALQAJZ5OP2HC7H7CDS')\n",
        "#.read_csv('https://raw.githubusercontent.com/userddssilva/Analise-COVID19-PP1-RNA2020.1/master/dataset/dataset_covid19_manaus.csv?token=AMHWGQPMBW2HL4RB3VNPKOC7H7CHE', encoding='ISO-8859-1', sep=';')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO36eUxqJck8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "68336afa-1ad3-430e-d624-88eb95ef1563"
      },
      "source": [
        "df_dataset = pd.read_csv('/content/dataset_limpo_covid19_manaus.csv')\n",
        "df_dataset"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idade</th>\n",
              "      <th>sexo</th>\n",
              "      <th>bairro</th>\n",
              "      <th>conclusao</th>\n",
              "      <th>dt_notificacao</th>\n",
              "      <th>tipo_teste</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>ADRIANOPOLIS</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>20/03/2020</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41</td>\n",
              "      <td>F</td>\n",
              "      <td>BETANIA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>27/03/2020</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>CIDADE NOVA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>27/04/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>44</td>\n",
              "      <td>M</td>\n",
              "      <td>SAO JORGE</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>27/04/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>F</td>\n",
              "      <td>COLONIA TERRA NOVA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>24/04/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6355</th>\n",
              "      <td>14</td>\n",
              "      <td>F</td>\n",
              "      <td>DA PAZ</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>25/06/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6356</th>\n",
              "      <td>5</td>\n",
              "      <td>F</td>\n",
              "      <td>REDENCAO</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>18/06/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6357</th>\n",
              "      <td>70</td>\n",
              "      <td>F</td>\n",
              "      <td>DOM PEDRO I</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>02/06/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTÍGENO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6358</th>\n",
              "      <td>68</td>\n",
              "      <td>M</td>\n",
              "      <td>JORGE TEIXEIRA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>01/06/2020</td>\n",
              "      <td>RT-PCR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6359</th>\n",
              "      <td>45</td>\n",
              "      <td>F</td>\n",
              "      <td>TARUMA</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>05/06/2020</td>\n",
              "      <td>TESTE RÁPIDO - ANTICORPO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6360 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      idade sexo  ... dt_notificacao                tipo_teste\n",
              "0        25    F  ...     20/03/2020                    RT-PCR\n",
              "1        41    F  ...     27/03/2020                    RT-PCR\n",
              "2        37    M  ...     27/04/2020  TESTE RÁPIDO - ANTICORPO\n",
              "3        44    M  ...     27/04/2020  TESTE RÁPIDO - ANTICORPO\n",
              "4        27    F  ...     24/04/2020  TESTE RÁPIDO - ANTICORPO\n",
              "...     ...  ...  ...            ...                       ...\n",
              "6355     14    F  ...     25/06/2020   TESTE RÁPIDO - ANTÍGENO\n",
              "6356      5    F  ...     18/06/2020   TESTE RÁPIDO - ANTÍGENO\n",
              "6357     70    F  ...     02/06/2020   TESTE RÁPIDO - ANTÍGENO\n",
              "6358     68    M  ...     01/06/2020                    RT-PCR\n",
              "6359     45    F  ...     05/06/2020  TESTE RÁPIDO - ANTICORPO\n",
              "\n",
              "[6360 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxLI9EUkOWUB",
        "colab_type": "text"
      },
      "source": [
        "**1. Construa um histograma denotando a quantidade de casos nos 10 bairros em que houve mais casos registrados. Inclua todos os bairros remanescentes em uma categoria denominada “Outros.” Denote as informações de maneira percentual.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN4EBBh9wv7g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2a31efb9-b1f3-45e8-85eb-e4037ddef8d0"
      },
      "source": [
        "x = df_dataset.bairro\n",
        "plt.figure(figsize=(30,10))\n",
        "plt.hist(x, bins=300, density=100)\n",
        "plt.ylabel('quantidade de casos')\n",
        "plt.xlabel('bairros')\n",
        "plt.show()\n",
        "\n",
        "values_by_bairro = df_dataset.bairro.value_counts()\n",
        "y = values_by_bairro[0:10].sort_values().values\n",
        "x = list(values_by_bairro[0:10].sort_values().keys())\n",
        "n_outros = sum(values_by_bairro[11:].sort_values().values)\n",
        "y = np.append(y, [n_outros])\n",
        "y = (y/sum(y))*100\n",
        "x.append('OUTROS')\n",
        "\n",
        "# Bring some raw data.\n",
        "frequencies = y\n",
        "# In my original code I create a series and run on that, \n",
        "# so for consistency I create a series from the list.\n",
        "freq_series = pd.Series(frequencies)\n",
        "\n",
        "x_labels = x\n",
        "\n",
        "# Plot the figure.\n",
        "plt.figure(figsize=(12, 8))\n",
        "ax = freq_series.plot(kind='bar')\n",
        "ax.set_title('Distribuição percentual dos bairros com mais casos')\n",
        "ax.set_xlabel('Bairros')\n",
        "ax.set_ylabel('Percentual')\n",
        "ax.set_xticklabels(x_labels)\n",
        "\n",
        "rects = ax.patches\n",
        "\n",
        "# Make some labels.\n",
        "labels = [\"%.2f%%\" % i for i in y]\n",
        "\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(rect.get_x() + rect.get_width() / 2, height + 5, label,\n",
        "            ha='center', va='bottom')\n",
        "plt.show()\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3iaDA84OwFA",
        "colab_type": "text"
      },
      "source": [
        "**2. Denote, por sexo, o boxplot da idade dos casos confirmados. Há outliers?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-PD1oxCw1Ia",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "1142d037-cc54-47f3-e3bd-8fed98f24fcd"
      },
      "source": [
        "fame = df_dataset[df_dataset['sexo'] == 'F'].idade.values\n",
        "male = df_dataset[df_dataset['sexo'] == 'M'].idade.values\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.boxplot([fame, male], labels=('Femino', 'Masculino'))\n",
        "plt.show()\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HHlHVltO31n",
        "colab_type": "text"
      },
      "source": [
        "**3. Denote em um gráfico de barras o número de novos casos por dia, considerando os 10 últimos dias na base de dados.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRtOqFZBy_XV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e4279eb4-fd10-496d-9fa5-61b6a2dbee60"
      },
      "source": [
        "dates = df_dataset\n",
        "\n",
        "#datas transformadas pro formato Ano-Mês-Dia\n",
        "dates['dt_notificacao'] = pd.to_datetime(dates['dt_notificacao'], dayfirst=True)\n",
        "\n",
        "#datas ordenadas\n",
        "dates = dates.sort_values(by='dt_notificacao')\n",
        "\n",
        "#obetenção do número de casos\n",
        "dates_cases = dates.groupby(['dt_notificacao']).size()\n",
        "\n",
        "n = dates_cases.size\n",
        "n = n-10\n",
        "\n",
        "cases = dates_cases[n:].values\n",
        "\n",
        "casos = []\n",
        "\n",
        "for i in cases:\n",
        "  casos.append(i)\n",
        "\n",
        "#salvando as 10 datas\n",
        "dates = dates.groupby(['dt_notificacao']).head(1)\n",
        "date = dates.dt_notificacao.values\n",
        "\n",
        "\n",
        "datas = []\n",
        "\n",
        "for i in range(len(date)-10,len(date)):\n",
        "  datas.append(date[i])\n",
        "\n",
        "#Transformando as datas para o formato dia/mês/ano\n",
        "for i in range(0,len(datas)):\n",
        "  data = pd.to_datetime(str(datas[i])) \n",
        "  datas[i] = data.strftime('%d/%m/%Y')\n",
        "\n",
        "#Plotando o gráfico\n",
        "plt.barh(datas, casos)\n",
        "\n",
        "plt.ylabel('Data da Notificação')\n",
        "plt.xlabel('Casos')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eputw4GpXRN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "95ce3b85-5d59-4ec4-8805-94c32ebb41ad"
      },
      "source": [
        "df_dataset.groupby(by=['dt_notificacao','conclusao']).count().reset_index().sort_values(by='dt_notificacao').tail(10)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dt_notificacao</th>\n",
              "      <th>conclusao</th>\n",
              "      <th>idade</th>\n",
              "      <th>sexo</th>\n",
              "      <th>bairro</th>\n",
              "      <th>tipo_teste</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>2020-07-02</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>2020-07-03</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>2020-07-04</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>2020-07-05</td>\n",
              "      <td>Recuperado</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>2020-07-16</td>\n",
              "      <td>Óbito</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>Óbito</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>Óbito</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>2020-07-30</td>\n",
              "      <td>Óbito</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>Óbito</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    dt_notificacao   conclusao  idade  sexo  bairro  tipo_teste\n",
              "118     2020-07-01  Recuperado     43    43      43          43\n",
              "119     2020-07-02  Recuperado     36    36      36          36\n",
              "120     2020-07-03  Recuperado     30    30      30          30\n",
              "121     2020-07-04  Recuperado      6     6       6           6\n",
              "122     2020-07-05  Recuperado      9     9       9           9\n",
              "123     2020-07-16       Óbito      1     1       1           1\n",
              "124     2020-07-20       Óbito      1     1       1           1\n",
              "125     2020-07-24       Óbito      2     2       2           2\n",
              "126     2020-07-30       Óbito      1     1       1           1\n",
              "127     2020-07-31       Óbito      1     1       1           1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kw8lsMM7O9pn",
        "colab_type": "text"
      },
      "source": [
        "**4. Repita o gráfico anterior considerando o número de casos recuperado.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjEeqru68zVO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1c4e55a7-2f48-4150-dc89-b3350528bd42"
      },
      "source": [
        "dates = df_dataset\n",
        "\n",
        "#datas transformadas pro formato Ano-Mês-Dia\n",
        "dates['dt_notificacao'] = pd.to_datetime(dates['dt_notificacao'], dayfirst=True)\n",
        "\n",
        "#datas ordenadas\n",
        "dates = dates.sort_values(by='dt_notificacao')\n",
        "\n",
        "#obetenção do número de casos\n",
        "dates_cases = dates.groupby(['dt_notificacao','conclusao']).size()\n",
        "\n",
        "n = dates_cases.size\n",
        "n = n-10\n",
        "\n",
        "cases = dates_cases[n:].values\n",
        "\n",
        "casos = []\n",
        "\n",
        "for i in cases:\n",
        "  casos.append(i)\n",
        "\n",
        "\n",
        "#obtendo status da recuperacao\n",
        "conclusao = []\n",
        "for i in dates.conclusao:\n",
        "  conclusao.append(i)\n",
        "\n",
        "tam = len(conclusao)-10\n",
        "conclusao = conclusao[tam:]\n",
        "\n",
        "#obter quantidade de recuperados\n",
        "df_casos_recuperados = dates.groupby(['dt_notificacao','conclusao']).size()\n",
        "df_casos_recuperados\n",
        "\n",
        "qtde_conclusoes = []\n",
        "\n",
        "for i in df_casos_recuperados:\n",
        "  qtde_conclusoes.append(i)\n",
        "\n",
        "qtde_conclusoes = qtde_conclusoes[len(qtde_conclusoes)-10:]\n",
        "\n",
        "#salvando as 10 datas\n",
        "dates = dates.groupby(['dt_notificacao','conclusao']).head(1)\n",
        "date = dates.dt_notificacao.values\n",
        "\n",
        "#salvando as 10 datas\n",
        "dates = dates.groupby(['dt_notificacao']).head(1)\n",
        "date = dates.dt_notificacao.values\n",
        "\n",
        "\n",
        "datas = []\n",
        "\n",
        "for i in range(len(date)-10,len(date)):\n",
        "  datas.append(date[i])\n",
        "\n",
        "#Transformando as datas para o formato dia/mês/ano\n",
        "for i in range(0,len(datas)):\n",
        "  data = pd.to_datetime(str(datas[i])) \n",
        "  datas[i] = data.strftime('%d/%m/%Y')\n",
        "\n",
        "df_index_conclusoes = dates.conclusao\n",
        "\n",
        "index_casos_recuperados = []\n",
        "\n",
        "for i in df_index_conclusoes:\n",
        "  index_casos_recuperados.append(i)\n",
        "\n",
        "tam2 = len(index_casos_recuperados)-10\n",
        "index_casos_recuperados = index_casos_recuperados[tam2:]\n",
        "\n",
        "label_recuperado = []\n",
        "for i in range(0,10):\n",
        "  if (index_casos_recuperados[i]==\"Recuperado\"):\n",
        "    label_recuperado.append(qtde_conclusoes[i])\n",
        "  else:\n",
        "    label_recuperado.append(0)\n",
        "\n",
        "#Plotando o gráfico\n",
        "plt.barh(datas, label_recuperado)\n",
        "\n",
        "plt.ylabel('Notificação do Caso')\n",
        "plt.xlabel('Casos Recuperados')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBBwiYZdPKqw",
        "colab_type": "text"
      },
      "source": [
        "**5. Construa um histograma que denote a quantidade percentual de casos por grupo etário, considerando que cada grupo contempla uma década (0 a 10 anos, 11 a 20 anos, etc.).**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ia50QTP80Xu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "e96d39f4-68f1-49fb-8c78-7dac830a398b"
      },
      "source": [
        "idades = df_dataset.idade.values\n",
        "max_val = idades.max()\n",
        "min_val = idades.min()\n",
        "\n",
        "amount_intervals = []\n",
        "a = 0\n",
        "k = []\n",
        "\n",
        "for intervalo in range(10, max_val+10, 10):\n",
        "    b = intervalo\n",
        "    amount_interval = df_dataset[(df_dataset['idade'] >= a) & (df_dataset['idade'] <= b)].shape[0]\n",
        "    amount_intervals.append(amount_interval)\n",
        "    k.append(b)\n",
        "    a = intervalo +1\n",
        "\n",
        "amount_intervals = np.array(amount_intervals)\n",
        "amount_intervals = (amount_intervals/sum(amount_intervals))*100\n",
        "\n",
        "x_labels = ['0-10'] + [str(i-10)+'-'+str(i) for i in k[1:]]\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(x_labels, amount_intervals, width=1)\n",
        "plt.xlabel('Intervalo das idades')\n",
        "plt.ylabel('Porcentagem de pessoas')\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3hb6fw2hPU3W",
        "colab_type": "text"
      },
      "source": [
        "**6. Elabore um gráfico que mostra o cumulativo de casos notificados ao longo do tempo.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8J23tAz_JHi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "1eee249c-65d5-4506-95ff-e687fc1c10f9"
      },
      "source": [
        "#obter as datas\n",
        "dates = df_dataset\n",
        "\n",
        "#datas transformadas pro formato Ano-Mês-Dia\n",
        "dates['dt_notificacao'] = pd.to_datetime(dates['dt_notificacao'], dayfirst=True)\n",
        "\n",
        "#datas ordenadas\n",
        "dates = dates.sort_values(by='dt_notificacao')\n",
        "\n",
        "date = dates.dt_notificacao.values\n",
        "\n",
        "# casos\n",
        "datas = []\n",
        "\n",
        "\n",
        "for i in range(0,len(date)):\n",
        "  datas.append(date[i])\n",
        "\n",
        "casos = []\n",
        "for i in range(0,len(datas)):\n",
        "  casos.append(1)\n",
        "\n",
        "for i in range(0,len(datas)):\n",
        "  if (i+1<len(datas)):\n",
        "    casos[i+1] = casos[i] + casos[i+1]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "\n",
        "ax.plot(datas,casos,label=\"casos\")\n",
        "ax.set_xlabel('Tempo (Dias)')\n",
        "ax.set_ylabel('Quantidade de Casos')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cjz4icH8PbgB",
        "colab_type": "text"
      },
      "source": [
        "**7. Faça um gráfico do tipo scatterplot que denote a idade versus o número total de casos registrado para aquela idade. Aproveite o processamento efetuado para o cálculo da correlação. É possível observar alguma tendência?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDXCnArq_KzM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "81473639-0c1f-4bc7-d1dd-22d536e61635"
      },
      "source": [
        "data = df_dataset.groupby(['idade']).size()\n",
        "y = data.index\n",
        "x = data.values\n",
        "\n",
        "plt.scatter(x,y)\n",
        "plt.ylabel('Idade')\n",
        "plt.xlabel('Total de casos')\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}