COVID_19.ipynb

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  "metadata": {
    "colab": {
      "name": "COVID-19.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNfSHO5BidUcuMJaRChTHo2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nascimento-luciano/Artificial-Intelligence/blob/master/COVID_19.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qA8B1j3n02HT"
      },
      "source": [
        "COVID-19 no Sudeste e no estado de São Paulo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVtdzUIj1PUh"
      },
      "source": [
        "Perguntas:\n",
        "\n",
        "-Quantos casos foram registrados nos estados do Sudeste?\n",
        "\n",
        "-Qual estado tem o maior número de casos?\n",
        "\n",
        "-Quantas mortes foram registradas nos estados do Sudeste?\n",
        "\n",
        "-Qual estado tem o maior número de mortes?\n",
        "\n",
        "-Como se comporta a curva de evolução nos estados do Sudeste?\n",
        "\n",
        "-Quais municípios tem o maior número de mortes registradas?\n",
        "\n",
        "-Como se comporta a curva de evolução nos municípios de São Paulo?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upXkbYyI2x5s"
      },
      "source": [
        "*Importando Bibliotecas*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fWQeE89ISqY"
      },
      "source": [
        "---------------------------------Etapa 1--------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReTYIcmB01ZI"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import geopandas as gpd\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 579,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ciV8NAMIGxCk",
        "outputId": "00133522-c6a6-4628-8055-c04c0a3c39cd"
      },
      "source": [
        "!pip install geopandas"
      ],
      "execution_count": 580,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.20)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.0)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.1.5)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.2.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2018.9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3aEyvoZ23V3"
      },
      "source": [
        "*Importando Arquivo CSV.* (Brasil)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5f2QfT22_Lg"
      },
      "source": [
        "#from google.colab import files\n",
        "#uploaded = files.upload()"
      ],
      "execution_count": 581,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CabiOzBBL4O"
      },
      "source": [
        "df = pd.read_csv ('brazil_covid19.csv')"
      ],
      "execution_count": 582,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUMdzott61C2"
      },
      "source": [
        "*Importando Arquivo CSV. (Cidades)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MceGMwf63Vu"
      },
      "source": [
        "#from google.colab import files\n",
        "#uploaded = files.upload()"
      ],
      "execution_count": 583,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcEMQpnPDvvb"
      },
      "source": [
        "dfs = pd.read_csv ('cases-brazil-cities.csv')"
      ],
      "execution_count": 584,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGUEgMxl67R-"
      },
      "source": [
        "*Importando Arquivo CSV. (Coordenadas)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN2EKCPu7BdP"
      },
      "source": [
        "#from google.colab import files\n",
        "#uploaded = files.upload()"
      ],
      "execution_count": 585,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VZhoKS-EBTz"
      },
      "source": [
        "dcs = pd.read_csv ('brazil_cities_coordinates.csv')"
      ],
      "execution_count": 586,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjZCHGpD7CEW"
      },
      "source": [
        "*Importando Arquivo CSV. (População)*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfl1Zsj77EmW"
      },
      "source": [
        "#from google.colab import files\n",
        "#uploaded = files.upload()"
      ],
      "execution_count": 587,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpPEIL58EP2K"
      },
      "source": [
        "#dhs = pd.read_csv('brazil_population_2019.csv', error_bad_lines=False)"
      ],
      "execution_count": 588,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOkeuJT0feKL"
      },
      "source": [
        "Importando Arquivo CSV. (São Paulo)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA16QVvsfiL5"
      },
      "source": [
        "dhs = pd.read_csv('covid_municipios.csv', error_bad_lines=False)"
      ],
      "execution_count": 605,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NJPZMqmfiKr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWUSgbq2Hpor"
      },
      "source": [
        "---------------------------------Etapa 2--------------------------------------\n",
        "\n",
        "Extração das Informções do Arquivo CSV."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "i7WSfr8kICzh",
        "outputId": "743a8fa2-1315-48e8-c046-7fa25ea31787"
      },
      "source": [
        "dfs.info()"
      ],
      "execution_count": 589,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5596 entries, 0 to 5595\n",
            "Data columns (total 14 columns):\n",
            " #   Column                           Non-Null Count  Dtype  \n",
            "---  ------                           --------------  -----  \n",
            " 0   country                          5596 non-null   object \n",
            " 1   state                            5596 non-null   object \n",
            " 2   city                             5596 non-null   object \n",
            " 3   ibgeID                           5596 non-null   int64  \n",
            " 4   deaths                           5596 non-null   int64  \n",
            " 5   totalCases                       5596 non-null   int64  \n",
            " 6   deaths_per_100k_inhabitants      5596 non-null   float64\n",
            " 7   totalCases_per_100k_inhabitants  5596 non-null   float64\n",
            " 8   deaths_by_totalCases             5596 non-null   float64\n",
            " 9   _source                          5596 non-null   object \n",
            " 10  date                             5596 non-null   object \n",
            " 11  newCases                         5596 non-null   int64  \n",
            " 12  newDeaths                        5596 non-null   int64  \n",
            " 13  last_info_date                   5596 non-null   object \n",
            "dtypes: float64(3), int64(5), object(6)\n",
            "memory usage: 612.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "y0ye8CzPH5Br",
        "outputId": "a2c04ace-f91b-4a32-9947-b73cec40d294"
      },
      "source": [
        "dcs.info()"
      ],
      "execution_count": 590,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5570 entries, 0 to 5569\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   state_code  5570 non-null   int64  \n",
            " 1   city_code   5570 non-null   int64  \n",
            " 2   city_name   5570 non-null   object \n",
            " 3   lat         5570 non-null   float64\n",
            " 4   long        5570 non-null   float64\n",
            " 5   capital     5570 non-null   bool   \n",
            "dtypes: bool(1), float64(2), int64(2), object(1)\n",
            "memory usage: 223.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fHZXYS3AFHyT",
        "outputId": "84dd9e2e-6bb5-4db8-fe72-f7f0bd4e9dbb"
      },
      "source": [
        "dhs.info()"
      ],
      "execution_count": 591,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2853 entries, 0 to 2852\n",
            "Data columns (total 8 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   region              2853 non-null   object\n",
            " 1   state               2853 non-null   object\n",
            " 2   city                2853 non-null   object\n",
            " 3   state_code          2853 non-null   int64 \n",
            " 4   city_code           2853 non-null   int64 \n",
            " 5   health_region_code  2853 non-null   int64 \n",
            " 6   health_region       2853 non-null   object\n",
            " 7   population          2853 non-null   int64 \n",
            "dtypes: int64(4), object(4)\n",
            "memory usage: 178.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GNSrZ2UDKaFz",
        "outputId": "fe0d2c38-2dc4-4680-bcae-aeea3cd89c23"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 592,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12258 entries, 0 to 12257\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    12258 non-null  object \n",
            " 1   region  12258 non-null  object \n",
            " 2   state   12258 non-null  object \n",
            " 3   cases   12258 non-null  float64\n",
            " 4   deaths  12258 non-null  int64  \n",
            "dtypes: float64(1), int64(1), object(3)\n",
            "memory usage: 479.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeSYKlN0IM-f"
      },
      "source": [
        "-----------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkGy74yhIM9Z"
      },
      "source": [
        "---------------------------------Etapa 3--------------------------------------\n",
        "\n",
        "Tabela de Casos e Mortes por Estado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auz-wVYWHT8b"
      },
      "source": [
        "dn = df.loc[df['region']=='Sudeste'].drop(columns=['region'])"
      ],
      "execution_count": 593,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "n2m1xEhoMDtt",
        "outputId": "e311df94-0413-43cb-e4aa-6807d7a90979"
      },
      "source": [
        "dn.sort_values(by='state',ascending=True)"
      ],
      "execution_count": 594,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>state</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>ES</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4772</th>\n",
              "      <td>2020-08-19</td>\n",
              "      <td>ES</td>\n",
              "      <td>103090.0</td>\n",
              "      <td>2963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4799</th>\n",
              "      <td>2020-08-20</td>\n",
              "      <td>ES</td>\n",
              "      <td>104037.0</td>\n",
              "      <td>2980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11063</th>\n",
              "      <td>2021-04-09</td>\n",
              "      <td>ES</td>\n",
              "      <td>400820.0</td>\n",
              "      <td>8079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4826</th>\n",
              "      <td>2020-08-21</td>\n",
              "      <td>ES</td>\n",
              "      <td>104911.0</td>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6611</th>\n",
              "      <td>2020-10-26</td>\n",
              "      <td>SP</td>\n",
              "      <td>1092843.0</td>\n",
              "      <td>38753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6638</th>\n",
              "      <td>2020-10-27</td>\n",
              "      <td>SP</td>\n",
              "      <td>1098207.0</td>\n",
              "      <td>38885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6665</th>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>SP</td>\n",
              "      <td>1103582.0</td>\n",
              "      <td>39007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6719</th>\n",
              "      <td>2020-10-30</td>\n",
              "      <td>SP</td>\n",
              "      <td>1113788.0</td>\n",
              "      <td>39255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12254</th>\n",
              "      <td>2021-05-23</td>\n",
              "      <td>SP</td>\n",
              "      <td>3188105.0</td>\n",
              "      <td>107614</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1816 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             date state      cases  deaths\n",
              "20     2020-02-25    ES        0.0       0\n",
              "4772   2020-08-19    ES   103090.0    2963\n",
              "4799   2020-08-20    ES   104037.0    2980\n",
              "11063  2021-04-09    ES   400820.0    8079\n",
              "4826   2020-08-21    ES   104911.0    3000\n",
              "...           ...   ...        ...     ...\n",
              "6611   2020-10-26    SP  1092843.0   38753\n",
              "6638   2020-10-27    SP  1098207.0   38885\n",
              "6665   2020-10-28    SP  1103582.0   39007\n",
              "6719   2020-10-30    SP  1113788.0   39255\n",
              "12254  2021-05-23    SP  3188105.0  107614\n",
              "\n",
              "[1816 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 594
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpaXY4hKMJ0A"
      },
      "source": [
        "dr = dn.sort_values(by=['date'],ascending=False)"
      ],
      "execution_count": 595,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59QByGROMLOx"
      },
      "source": [
        "rn = dr.head(9).drop(columns=['date']).sort_values(by='cases',ascending=False)"
      ],
      "execution_count": 596,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "YuqzKKwoM2z5",
        "outputId": "e976af27-90f5-4a4e-bf2e-905ea7534be6"
      },
      "source": [
        "#Plotagem em gráfico de barras os estados com maior número de casos de Covid-19\n",
        "figrn = px.bar(rn, x='state',y='cases',\n",
        "labels={'cases':'CASOS','state':'ESTADOS'},title='COVID-19 Ranking Região Sudeste - Casos')\n",
        "figrn.show()"
      ],
      "execution_count": 597,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
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      "metadata": {
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          "height": 542
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        "id": "T2v8RFWTOqT7",
        "outputId": "9d807eca-bed3-4ad3-fec8-b1f6a7c8ba3a"
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      "source": [
        "#Plotagem em gráfico de barras os estados com maior número de óbitos de Covid-19\n",
        "figrn = px.bar(rn, x='state', y='deaths',\n",
        "labels={'deaths':'ÓBITOS','state':'ESTADOS'},title='COVID-19 Ranking Região Sudeste - Óbitos')\n",
        "figrn.show()"
      ],
      "execution_count": 598,
      "outputs": [
        {
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    {
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      "metadata": {
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        "id": "1DQjtzelO-SQ",
        "outputId": "f6640ebf-cc7a-4524-9800-52d561a9b7db"
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      "source": [
        "#Plotagem em gráfico de linha em relação ao número de evolução em casos de Covid-19\n",
        "figrn = px.line(dn, x='date',y='cases',\n",
        "color='state',labels={'cases':'CASOS','state':'ESTADOS','date':'TEMPO'},title='Curva de Evolução de COVID-19 no Sudeste')\n",
        "figrn.show()"
      ],
      "execution_count": 599,
      "outputs": [
        {
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        "id": "KrGgwm8HPQ-_",
        "outputId": "fb886eac-e452-4573-988b-2eab58ecc635"
      },
      "source": [
        "df.info()\n",
        "#dfs.info()\n",
        "#dhs.info()\n",
        "#dcs.info()"
      ],
      "execution_count": 600,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12258 entries, 0 to 12257\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    12258 non-null  object \n",
            " 1   region  12258 non-null  object \n",
            " 2   state   12258 non-null  object \n",
            " 3   cases   12258 non-null  float64\n",
            " 4   deaths  12258 non-null  int64  \n",
            "dtypes: float64(1), int64(1), object(3)\n",
            "memory usage: 479.0+ KB\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rD9S7JsePWVv"
      },
      "source": [
        "ds = dfs.loc[dfs['state']=='SP']\n",
        "cm = dhs.loc[dhs['state']=='São Paulo']\n",
        "coo = dcs.loc[dcs['state_code']==28]"
      ],
      "execution_count": 601,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EDpU6fQU60l"
      },
      "source": [
        "rs = ds.sort_values(by=['deaths','city'],ascending=False).head()"
      ],
      "execution_count": 602,
      "outputs": []
    },
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        "outputId": "2c3ca16c-eb03-4f9e-b6c4-d6cdf7b8f4d2"
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        "#Plotagem em gráfico de barras as cidades com maior número de óbitos de Covid-19\n",
        "figrs = px.bar(rs, x='city', y='deaths',\n",
        "labels={'deaths':'ÓBITOS','city':'MUNICÍPIOS'},title='TOP 5 COVID-19 SÃO PAULO - Óbitos')\n",
        "figrs.show()"
      ],
      "execution_count": 603,
      "outputs": [
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      "source": [
        "------------------------------------------------------------------------------"
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}