dadoscovid_ourworlddata.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMaQeGc2j5AfT7ykqm+Vckt",
      "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/nadiduno/linkColabGoogle/blob/main/dadoscovid_ourworlddata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este código utiliza a biblioteca pandas para carregar e manipular os dados, matplotlib e seaborn para visualizações e estatísticas descritivas. Ele carrega dados públicos da pandemia COVID-19 do repositório do Our World in Data, exibe os primeiros registros dos dados, fornece informações sobre os tipos de dados e valores ausentes, calcula estatísticas descritivas para variáveis numéricas e cria visualizações básicas dos dados."
      ],
      "metadata": {
        "id": "7t8wfy-DiTwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "JeEbWJOBfiAl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Carregar os dados\n",
        "url = \"https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv\"\n",
        "dados_covid = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "gHviwDwlfb1k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Visualização dos primeiros registros dos dados\n",
        "print(\"Visualização dos primeiros registros dos dados:\")\n",
        "print(dados_covid.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_D-6HJwMftoz",
        "outputId": "ee599933-629c-4171-ceee-f8209fa00a3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualização dos primeiros registros dos dados:\n",
            "  iso_code continent     location        date  total_cases  new_cases  \\\n",
            "0      AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
            "1      AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
            "2      AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
            "3      AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
            "4      AFG      Asia  Afghanistan  2020-01-09          NaN        0.0   \n",
            "\n",
            "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
            "0                 NaN           NaN         0.0                  NaN  ...   \n",
            "1                 NaN           NaN         0.0                  NaN  ...   \n",
            "2                 NaN           NaN         0.0                  NaN  ...   \n",
            "3                 NaN           NaN         0.0                  NaN  ...   \n",
            "4                 NaN           NaN         0.0                  NaN  ...   \n",
            "\n",
            "   male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
            "0           NaN                  37.746                         0.5   \n",
            "1           NaN                  37.746                         0.5   \n",
            "2           NaN                  37.746                         0.5   \n",
            "3           NaN                  37.746                         0.5   \n",
            "4           NaN                  37.746                         0.5   \n",
            "\n",
            "   life_expectancy  human_development_index  population  \\\n",
            "0            64.83                    0.511  41128772.0   \n",
            "1            64.83                    0.511  41128772.0   \n",
            "2            64.83                    0.511  41128772.0   \n",
            "3            64.83                    0.511  41128772.0   \n",
            "4            64.83                    0.511  41128772.0   \n",
            "\n",
            "   excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
            "0                                   NaN                          NaN   \n",
            "1                                   NaN                          NaN   \n",
            "2                                   NaN                          NaN   \n",
            "3                                   NaN                          NaN   \n",
            "4                                   NaN                          NaN   \n",
            "\n",
            "   excess_mortality  excess_mortality_cumulative_per_million  \n",
            "0               NaN                                      NaN  \n",
            "1               NaN                                      NaN  \n",
            "2               NaN                                      NaN  \n",
            "3               NaN                                      NaN  \n",
            "4               NaN                                      NaN  \n",
            "\n",
            "[5 rows x 67 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar informações sobre os tipos de dados e valores ausentes\n",
        "print(\"\\nInformações sobre os tipos de dados e valores ausentes:\")\n",
        "print(dados_covid.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDz2LtBYf2cF",
        "outputId": "2ba825c6-ddfd-45f4-a605-3ddbb27b9317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Informações sobre os tipos de dados e valores ausentes:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 380288 entries, 0 to 380287\n",
            "Data columns (total 67 columns):\n",
            " #   Column                                      Non-Null Count   Dtype  \n",
            "---  ------                                      --------------   -----  \n",
            " 0   iso_code                                    380288 non-null  object \n",
            " 1   continent                                   362117 non-null  object \n",
            " 2   location                                    380288 non-null  object \n",
            " 3   date                                        380288 non-null  object \n",
            " 4   total_cases                                 341501 non-null  float64\n",
            " 5   new_cases                                   369504 non-null  float64\n",
            " 6   new_cases_smoothed                          368274 non-null  float64\n",
            " 7   total_deaths                                319445 non-null  float64\n",
            " 8   new_deaths                                  369511 non-null  float64\n",
            " 9   new_deaths_smoothed                         368281 non-null  float64\n",
            " 10  total_cases_per_million                     341501 non-null  float64\n",
            " 11  new_cases_per_million                       369504 non-null  float64\n",
            " 12  new_cases_smoothed_per_million              368274 non-null  float64\n",
            " 13  total_deaths_per_million                    319445 non-null  float64\n",
            " 14  new_deaths_per_million                      369511 non-null  float64\n",
            " 15  new_deaths_smoothed_per_million             368281 non-null  float64\n",
            " 16  reproduction_rate                           184817 non-null  float64\n",
            " 17  icu_patients                                38476 non-null   float64\n",
            " 18  icu_patients_per_million                    38476 non-null   float64\n",
            " 19  hosp_patients                               39982 non-null   float64\n",
            " 20  hosp_patients_per_million                   39982 non-null   float64\n",
            " 21  weekly_icu_admissions                       10613 non-null   float64\n",
            " 22  weekly_icu_admissions_per_million           10613 non-null   float64\n",
            " 23  weekly_hosp_admissions                      24029 non-null   float64\n",
            " 24  weekly_hosp_admissions_per_million          24029 non-null   float64\n",
            " 25  total_tests                                 79387 non-null   float64\n",
            " 26  new_tests                                   75403 non-null   float64\n",
            " 27  total_tests_per_thousand                    79387 non-null   float64\n",
            " 28  new_tests_per_thousand                      75403 non-null   float64\n",
            " 29  new_tests_smoothed                          103965 non-null  float64\n",
            " 30  new_tests_smoothed_per_thousand             103965 non-null  float64\n",
            " 31  positive_rate                               95927 non-null   float64\n",
            " 32  tests_per_case                              94348 non-null   float64\n",
            " 33  tests_units                                 106788 non-null  object \n",
            " 34  total_vaccinations                          82801 non-null   float64\n",
            " 35  people_vaccinated                           78736 non-null   float64\n",
            " 36  people_fully_vaccinated                     75598 non-null   float64\n",
            " 37  total_boosters                              50890 non-null   float64\n",
            " 38  new_vaccinations                            68589 non-null   float64\n",
            " 39  new_vaccinations_smoothed                   189312 non-null  float64\n",
            " 40  total_vaccinations_per_hundred              82801 non-null   float64\n",
            " 41  people_vaccinated_per_hundred               78736 non-null   float64\n",
            " 42  people_fully_vaccinated_per_hundred         75598 non-null   float64\n",
            " 43  total_boosters_per_hundred                  50890 non-null   float64\n",
            " 44  new_vaccinations_smoothed_per_million       189312 non-null  float64\n",
            " 45  new_people_vaccinated_smoothed              186775 non-null  float64\n",
            " 46  new_people_vaccinated_smoothed_per_hundred  186775 non-null  float64\n",
            " 47  stringency_index                            197292 non-null  float64\n",
            " 48  population_density                          323229 non-null  float64\n",
            " 49  median_age                                  300482 non-null  float64\n",
            " 50  aged_65_older                               290097 non-null  float64\n",
            " 51  aged_70_older                               297470 non-null  float64\n",
            " 52  gdp_per_capita                              294615 non-null  float64\n",
            " 53  extreme_poverty                             189898 non-null  float64\n",
            " 54  cardiovasc_death_rate                       295189 non-null  float64\n",
            " 55  diabetes_prevalence                         310386 non-null  float64\n",
            " 56  female_smokers                              221553 non-null  float64\n",
            " 57  male_smokers                                218541 non-null  float64\n",
            " 58  handwashing_facilities                      144615 non-null  float64\n",
            " 59  hospital_beds_per_thousand                  260709 non-null  float64\n",
            " 60  life_expectancy                             350180 non-null  float64\n",
            " 61  human_development_index                     286290 non-null  float64\n",
            " 62  population                                  380288 non-null  float64\n",
            " 63  excess_mortality_cumulative_absolute        13113 non-null   float64\n",
            " 64  excess_mortality_cumulative                 13113 non-null   float64\n",
            " 65  excess_mortality                            13113 non-null   float64\n",
            " 66  excess_mortality_cumulative_per_million     13113 non-null   float64\n",
            "dtypes: float64(62), object(5)\n",
            "memory usage: 194.4+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatísticas descritivas para variáveis numéricas\n",
        "print(\"\\nEstatísticas descritivas para variáveis numéricas:\")\n",
        "print(dados_covid.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjJtkgvBf9cF",
        "outputId": "8c60842c-3ec9-451e-a002-4e2467c29cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Estatísticas descritivas para variáveis numéricas:\n",
            "        total_cases     new_cases  new_cases_smoothed  total_deaths  \\\n",
            "count  3.415010e+05  3.695040e+05        3.682740e+05  3.194450e+05   \n",
            "mean   7.265121e+06  8.885927e+03        8.915033e+03  8.926562e+04   \n",
            "std    4.343297e+07  2.418897e+05        9.120534e+04  4.537741e+05   \n",
            "min    1.000000e+00  0.000000e+00        0.000000e+00  1.000000e+00   \n",
            "25%    9.109000e+03  0.000000e+00        0.000000e+00  1.340000e+02   \n",
            "50%    7.820500e+04  0.000000e+00        1.828600e+01  1.380000e+03   \n",
            "75%    8.385200e+05  0.000000e+00        4.200000e+02  1.238000e+04   \n",
            "max    7.746908e+08  4.423623e+07        6.319461e+06  7.031395e+06   \n",
            "\n",
            "          new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
            "count  369511.000000        368281.000000            341501.000000   \n",
            "mean       79.042832            79.304923            110506.216781   \n",
            "std      1437.554106           539.272961            158573.653271   \n",
            "min         0.000000             0.000000                 0.000000   \n",
            "25%         0.000000             0.000000              2901.115000   \n",
            "50%         0.000000             0.143000             32037.822000   \n",
            "75%         0.000000             4.286000            152913.774000   \n",
            "max    103719.000000         14817.000000            764359.624000   \n",
            "\n",
            "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
            "count          369504.000000                   368274.000000   \n",
            "mean              134.948898                      135.390849   \n",
            "std              1558.299411                      576.487021   \n",
            "min                 0.000000                        0.000000   \n",
            "25%                 0.000000                        0.000000   \n",
            "50%                 0.000000                        4.595000   \n",
            "75%                 0.000000                       70.915000   \n",
            "max            240325.866000                    34332.267000   \n",
            "\n",
            "       total_deaths_per_million  ...   male_smokers  handwashing_facilities  \\\n",
            "count             319445.000000  ...  218541.000000           144615.000000   \n",
            "mean                 910.215000  ...      32.909853               50.790542   \n",
            "std                 1126.342259  ...      13.572579               31.954150   \n",
            "min                    0.000000  ...       7.700000                1.188000   \n",
            "25%                   64.966000  ...      22.600000               20.859000   \n",
            "50%                  407.439000  ...      33.100000               49.839000   \n",
            "75%                 1445.813000  ...      41.300000               82.502000   \n",
            "max                 6507.656000  ...      78.100000              100.000000   \n",
            "\n",
            "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
            "count               260709.000000    350180.000000            286290.000000   \n",
            "mean                     3.097395        73.712581                 0.722556   \n",
            "std                      2.548265         7.395331                 0.149001   \n",
            "min                      0.100000        53.280000                 0.394000   \n",
            "25%                      1.300000        69.590000                 0.602000   \n",
            "50%                      2.500000        75.050000                 0.740000   \n",
            "75%                      4.200000        79.460000                 0.829000   \n",
            "max                     13.800000        86.750000                 0.957000   \n",
            "\n",
            "         population  excess_mortality_cumulative_absolute  \\\n",
            "count  3.802880e+05                          1.311300e+04   \n",
            "mean   1.289767e+08                          5.460328e+04   \n",
            "std    6.624295e+08                          1.513266e+05   \n",
            "min    4.700000e+01                         -3.772610e+04   \n",
            "25%    4.490020e+05                          1.708000e+02   \n",
            "50%    5.882259e+06                          6.654600e+03   \n",
            "75%    2.830170e+07                          3.875370e+04   \n",
            "max    7.975105e+09                          1.327828e+06   \n",
            "\n",
            "       excess_mortality_cumulative  excess_mortality  \\\n",
            "count                 13113.000000      13113.000000   \n",
            "mean                      9.752126         10.987011   \n",
            "std                      11.973323         24.567275   \n",
            "min                     -44.230000        -95.920000   \n",
            "25%                       1.990000         -1.520000   \n",
            "50%                       8.150000          5.650000   \n",
            "75%                      15.060000         15.720000   \n",
            "max                      76.620000        377.700000   \n",
            "\n",
            "       excess_mortality_cumulative_per_million  \n",
            "count                             13113.000000  \n",
            "mean                               1768.995621  \n",
            "std                                1986.543673  \n",
            "min                               -2936.453100  \n",
            "25%                                 115.941200  \n",
            "50%                                1237.869900  \n",
            "75%                                2871.024400  \n",
            "max                               10293.515000  \n",
            "\n",
            "[8 rows x 62 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizações básicas\n",
        "plt.figure(figsize=(12, 6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "HDLI1D0FgDK0",
        "outputId": "ea6f3828-0c79-4deb-d267-89405c2c089f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualização da evolução dos casos confirmados ao longo do tempo\n",
        "plt.subplot(1, 2, 1)\n",
        "dados_covid.plot(x='date', y='total_cases', color='blue', title='Evolução dos Casos Confirmados', legend=False)\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Total de Casos Confirmados')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "PUdtyZeHgG5n",
        "outputId": "55e06b1d-5ec0-4a60-c2a0-d30bb8eb9894"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Total de Casos Confirmados')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gráfico de barras mostrando os 10 países com mais mortes\n",
        "top_10_paises_mortes = dados_covid.groupby('location')['total_deaths'].max().sort_values(ascending=False).head(10)\n",
        "plt.subplot(1, 2, 2)\n",
        "top_10_paises_mortes.plot(kind='bar', color='red')\n",
        "plt.title('Top 10 Países com Mais Mortes')\n",
        "plt.xlabel('País')\n",
        "plt.ylabel('Total de Mortes')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "FCLXyAIqgLiy",
        "outputId": "57e50198-26d3-43fb-ed0f-b6a22d0ff05d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}