Panorama_do_COVID_19.ipynb

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        "# Panorama do COVID-19\n",
        "\n",
        "A COVID-19 emergiu como uma preocupação global desde que o novo coronavírus, conhecido como SARS-CoV-2, foi identificado pela primeira vez em Wuhan, China, no final de 2019. O alerta inicial foi dado pela Organização Mundial da Saúde (OMS) em 31 de dezembro daquele ano, quando um conjunto de casos de \"pneumonia viral\" foi relatado.\n",
        "\n",
        "A propagação desse vírus ocorre principalmente por meio de gotículas respiratórias expelidas durante tosses, espirros e até mesmo ao falar. Essas gotículas, quando inaladas por indivíduos saudáveis, podem resultar na infecção pelo vírus. A complexidade da COVID-19 se manifesta na ampla gama de sintomas que varia de pessoa para pessoa, desde casos assintomáticos até condições graves que requerem cuidados médicos intensivos.\n",
        "\n",
        "<img alt=\"Colaboratory logo\" width=\"80%\" src=\"https://static.poder360.com.br/2022/12/coronavirus-imagem-1-848x477.jpg\">\n",
        "\n",
        "Até o presente momento, observa-se que cerca de 80% dos casos confirmados são assintomáticos e rápidos. A maioria das pessoas que se encaixam nesse grupo, se recupera sem nenhuma sequela.\n",
        "\n",
        "No entanto, 15% das pessoas terão infecções graves e precisarão de oxigênio. O restante das pessoas, que representam 5%, serão classificadas como infecções muito graves e precisarão de ventilação assistida, por meio de respiradores mecânicos em ambiente hospitalar.\n",
        "\n",
        "Com o objetivo de elevar a consciência situacional a respeito do COVID-19 no Brasil, irei realizar uma análise sobre os dados públicos da doença."
      ]
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        "## Obtenção dos Dados\n",
        "\n",
        "Os dados utilizados neste conjunto são fornecidos pelo Our World in Data (OWID), uma iniciativa colaborativa que visa tornar os dados sobre questões globais, como saúde, desenvolvimento e meio ambiente, acessíveis e compreensíveis para todos. O OWID coleta dados de várias fontes confiáveis, como organizações de saúde nacionais e internacionais, governos, e agências de estatísticas. Eles são conhecidos por sua transparência e rigor na coleta e análise de dados, o que os torna uma fonte confiável para pesquisadores, jornalistas e tomadores de decisão em todo o mundo.\n",
        "\n",
        "Este conjunto de dados abrange uma ampla gama de métricas relacionadas à pandemia de COVID-19, incluindo casos confirmados, mortes, testes, vacinações, medidas governamentais de resposta, características demográficas e socioeconômicas, e muito mais. Ele oferece uma visão abrangente da propagação da doença e de suas consequências em diferentes países e regiões ao redor do mundo.\n",
        "\n",
        "Além dos números básicos de casos e mortes, o dataset também inclui informações sobre a capacidade de resposta dos sistemas de saúde, como o número de leitos hospitalares disponíveis, e fatores de risco relacionados à saúde, como prevalência de doenças crônicas e hábitos de tabagismo.\n",
        "\n",
        "Os dados são atualizados regularmente e são apresentados de forma organizada e acessível, facilitando análises comparativas entre países e ao longo do tempo.\n",
        "<br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br>\n",
        "\n",
        "Analisar uma doença nova, como foi o caso da COVID-19, apresenta uma série de desafios significativos devido à natureza desconhecida e em rápida evolução da doença. Algumas das principais dificuldades incluem:\n",
        "\n",
        "1.   Falta de Dados Consistentes e Confiança: No início de uma pandemia, os dados disponíveis podem ser limitados, incompletos ou inconsistentes. Isso pode dificultar a compreensão da extensão da propagação da doença e a formulação de estratégias eficazes de prevenção e controle.\n",
        "\n",
        "2.   Mudanças nas Diretrizes e Protocolos: À medida que os cientistas aprendem mais sobre a doença e novas evidências emergem, as diretrizes e protocolos para diagnóstico, tratamento e relatórios de dados podem mudar. Isso pode levar a discrepâncias nos números relatados ao longo do tempo e entre diferentes fontes.\n",
        "\n",
        "3. Variação na Testagem e Notificação: A capacidade de testagem e a abrangência da notificação de casos podem variar amplamente entre diferentes regiões e países. Isso pode resultar em subnotificação de casos reais e distorcer a imagem verdadeira da propagação da doença.\n",
        "\n",
        "4. Interesses Políticos e Econômicos: Em alguns casos, os governos podem ter incentivos para minimizar ou exagerar a gravidade da situação por motivos políticos, econômicos ou de imagem pública. Isso pode levar a conflitos entre os dados oficiais divulgados pelos governos federal e estaduais e os dados independentes coletados por organizações não governamentais ou pela comunidade científica.\n",
        "\n",
        "5. Desafios na Padronização e Harmonização dos Dados: Mesmo quando os dados são relatados de forma consistente pelos governos, pode haver desafios na padronização e harmonização dos dados entre diferentes jurisdições e países. Isso pode dificultar a comparação direta e a análise agregada dos dados.\n",
        "\n",
        "\n",
        "\n",
        "No contexto da COVID-19, esses desafios foram amplificados pela velocidade com que a doença se espalhou globalmente e pela necessidade urgente de tomar decisões informadas para conter sua propagação. Portanto, a análise de uma doença nova como a COVID-19 requer uma abordagem cuidadosa e crítica, levando em consideração as limitações e incertezas associadas aos dados disponíveis.<br><br>"
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        "## Dicionário de Variáveis\n",
        "\n",
        "<ul><li><code>iso_code</code>: Códigos de país de três letras do ISO 3166-1 alpha-3. Note que as regiões definidas pelo OWID (por exemplo, continentes como 'Europa') contêm o prefixo 'OWID_'.</li><li><code>continent</code>: Continente da localização geográfica.</li><li><code>location</code>: Localização geográfica.</li><li><code>date</code>: Data da observação.</li><li><code>total_cases</code>: Total de casos confirmados de COVID-19. Os números podem incluir casos prováveis, quando relatados.</li><li><code>new_cases</code>: Novos casos confirmados de COVID-19. Os números podem incluir casos prováveis, quando relatados. Em casos raros em que nossa fonte relata uma mudança diária negativa devido a uma correção de dados, definimos essa métrica como NA.</li><li><code>new_cases_smoothed</code>: Novos casos confirmados de COVID-19 (suavizados em média móvel de 7 dias). Os números podem incluir casos prováveis, quando relatados.</li><li><code>total_deaths</code>: Total de mortes atribuídas ao COVID-19. Os números podem incluir mortes prováveis, quando relatados.</li><li><code>new_deaths</code>: Novas mortes atribuídas ao COVID-19. Os números podem incluir mortes prováveis, quando relatados. Em casos raros em que nossa fonte relata uma mudança diária negativa devido a uma correção de dados, definimos essa métrica como NA.</li><li><code>new_deaths_smoothed</code>: Novas mortes atribuídas ao COVID-19 (suavizadas em média móvel de 7 dias). Os números podem incluir mortes prováveis, quando relatados.</li><li><code>total_cases_per_million</code>: Total de casos confirmados de COVID-19 por 1.000.000 de pessoas. Os números podem incluir casos prováveis, quando relatados.</li><li><code>new_cases_per_million</code>: Novos casos confirmados de COVID-19 por 1.000.000 de pessoas. Os números podem incluir casos prováveis, quando relatados.</li><li><code>new_cases_smoothed_per_million</code>: Novos casos confirmados de COVID-19 (suavizados em média móvel de 7 dias) por 1.000.000 de pessoas. Os números podem incluir casos prováveis, quando relatados.</li><li><code>total_deaths_per_million</code>: Total de mortes atribuídas ao COVID-19 por 1.000.000 de pessoas. Os números podem incluir mortes prováveis, quando relatados.</li><li><code>new_deaths_per_million</code>: Novas mortes atribuídas ao COVID-19 por 1.000.000 de pessoas. Os números podem incluir mortes prováveis, quando relatados.</li><li><code>new_deaths_smoothed_per_million</code>: Novas mortes atribuídas ao COVID-19 (suavizadas em média móvel de 7 dias) por 1.000.000 de pessoas. Os números podem incluir mortes prováveis, quando relatados.</li><li><code>reproduction_rate</code>: Estimativa em tempo real da taxa efetiva de reprodução (R) do COVID-19. Consulte <a target=\"_new\" href=\"https://github.com/crondonm/TrackingR/tree/main/Estimates-Database\">https://github.com/crondonm/TrackingR/tree/main/Estimates-Database</a>.</li><li><code>icu_patients</code>: Número de pacientes com COVID-19 em unidades de terapia intensiva (UTIs) em um determinado dia.</li><li><code>icu_patients_per_million</code>: Número de pacientes com COVID-19 em unidades de terapia intensiva (UTIs) em um determinado dia por 1.000.000 de pessoas.</li><li><code>hosp_patients</code>: Número de pacientes com COVID-19 no hospital em um determinado dia.</li><li><code>hosp_patients_per_million</code>: Número de pacientes com COVID-19 no hospital em um determinado dia por 1.000.000 de pessoas.</li><li><code>weekly_icu_admissions</code>: Número de pacientes com COVID-19 recém-admitidos em unidades de terapia intensiva (UTIs) em uma semana específica (data de relatório e os 6 dias anteriores).</li><li><code>weekly_icu_admissions_per_million</code>: Número de pacientes com COVID-19 recém-admitidos em unidades de terapia intensiva (UTIs) em uma semana específica por 1.000.000 de pessoas (data de relatório e os 6 dias anteriores).</li><li><code>weekly_hosp_admissions</code>: Número de pacientes com COVID-19 recém-admitidos no hospital em uma semana específica (data de relatório e os 6 dias anteriores).</li><li><code>weekly_hosp_admissions_per_million</code>: Número de pacientes com COVID-19 recém-admitidos no hospital em uma semana específica por 1.000.000 de pessoas (data de relatório e os 6 dias anteriores).</li><li><code>total_tests</code>: Total de testes para COVID-19.</li><li><code>new_tests</code>: Novos testes para COVID-19 (calculados apenas para dias consecutivos).</li><li><code>total_tests_per_thousand</code>: Total de testes para COVID-19 por 1.000 pessoas.</li><li><code>new_tests_per_thousand</code>: Novos testes para COVID-19 por 1.000 pessoas.</li><li><code>new_tests_smoothed</code>: Novos testes para COVID-19 (suavizados em média móvel de 7 dias). Para países que não relatam dados de teste diariamente, assumimos que os testes mudaram igualmente diariamente durante quaisquer períodos em que nenhum dado foi relatado. Isso produz uma série completa de números diários, que é então calculada em uma janela de 7 dias.</li><li><code>new_tests_smoothed_per_thousand</code>: Novos testes para COVID-19 (suavizados em média móvel de 7 dias) por 1.000 pessoas.</li><li><code>positive_rate</code>: A parcela de testes COVID-19 que são positivos, fornecida como uma média móvel de 7 dias (este é o inverso de testes_por_caso).</li><li><code>tests_per_case</code>: Testes realizados por novo caso confirmado de COVID-19, fornecido como uma média móvel de 7 dias (este é o inverso de positive_rate).</li><li><code>tests_units</code>: Unidades usadas pela localização para relatar seus dados de teste. Um arquivo de país não pode conter unidades mistas. Todas as métricas referentes a dados de teste usam a unidade de teste especificada. As unidades válidas são 'pessoas testadas' (número de pessoas testadas), 'testes realizados' (número de testes realizados, uma única pessoa pode ser testada mais de uma vez em um determinado dia) e 'amostras testadas' (número de amostras testadas. Em alguns casos, mais de uma amostra pode ser necessária para realizar um determinado teste).</li><li><code>total_vaccinations</code>: Número total de doses de vacinação COVID-19 administradas.</li><li><code>people_vaccinated</code>: Número total de pessoas que receberam pelo menos uma dose da vacina.</li><li><code>people_fully_vaccinated</code>: Número total de pessoas que receberam todas as doses prescritas pelo protocolo de vacinação inicial.</li><li><code>total_boosters</code>: Número total de doses de reforço de vacinação COVID-19 administradas (doses administradas além do número prescrito pelo protocolo de vacinação).</li><li><code>new_vaccinations</code>: Novas doses de vacinação COVID-19 administradas (calculadas apenas para dias consecutivos).</li><li><code>new_vaccinations_smoothed</code>: Novas doses de vacinação COVID-19 administradas (suavizadas em média móvel de 7 dias). Para países que não relatam dados de vacinação diariamente, assumimos que a vacinação mudou igualmente diariamente durante quaisquer períodos em que nenhum dado foi relatado. Isso produz uma série completa de números diários, que é então calculada em uma janela de 7 dias.</li><li><code>total_vaccinations_per_hundred</code>: Número total de doses de vacinação COVID-19 administradas por 100 pessoas na população total.</li><li><code>people_vaccinated_per_hundred</code>: Número total de pessoas que receberam pelo menos uma dose da vacina por 100 pessoas na população total.</li><li><code>people_fully_vaccinated_per_hundred</code>: Número total de pessoas que receberam todas as doses prescritas pelo protocolo de vacinação inicial por 100 pessoas na população total.</li><li><code>total_boosters_per_hundred</code>: Número total de doses de reforço de vacinação COVID-19 administradas por 100 pessoas na população total.</li><li><code>new_vaccinations_smoothed_per_million</code>: Novas doses de vacinação COVID-19 administradas (suavizadas em média móvel de 7 dias) por 1.000.000 de pessoas na população total.</li><li><code>new_people_vaccinated_smoothed</code>: Número diário de pessoas recebendo sua primeira dose de vacina (suavizada em média móvel de 7 dias).</li><li><code>new_people_vaccinated_smoothed_per_hundred</code>: Número diário de pessoas recebendo sua primeira dose de vacina (suavizada em média móvel de 7 dias) por 100 pessoas na população total.</li><li><code>stringency_index</code>: Índice de Rigor da Resposta do Governo: medida composta com base em 9 indicadores de resposta, incluindo fechamento de escolas, fechamento de locais de trabalho e proibições de viagens, reescalonada para um valor de 0 a 100 (100 = resposta mais rigorosa).</li><li><code>population</code>: População (valores mais recentes disponíveis). Consulte <a target=\"_new\" href=\"https://github.com/owid/covid-19-data/blob/master/scripts/input/un/population_latest.csv\">https://github.com/owid/covid-19-data/blob/master/scripts/input/un/population_latest.csv</a> para lista completa de fontes.</li><li><code>population_density</code>: Número de pessoas dividido pela área terrestre, medido em quilômetros quadrados, ano mais recente disponível.</li><li><code>median_age</code>: Idade média da população, projeção da ONU para 2020.</li><li><code>aged_65_older</code>: Porcentagem da população com 65 anos ou mais, ano mais recente disponível.</li><li><code>aged_70_older</code>: Porcentagem da população com 70 anos ou mais em 2015.</li><li><code>gdp_per_capita</code>: Produto interno bruto (PIB) em paridade de poder de compra (dólares internacionais constantes de 2011), ano mais recente disponível.</li><li><code>extreme_poverty</code>: Porcentagem da população vivendo em extrema pobreza, ano mais recente disponível desde 2010.</li><li><code>cardiovasc_death_rate</code>: Taxa de mortalidade por doenças cardiovasculares em 2017 (número anual de mortes por 100.000 pessoas).</li><li><code>diabetes_prevalence</code>: Prevalência de diabetes (% da população com idade entre 20 e 79 anos) em 2017.</li><li><code>female_smokers</code>: Porcentagem de mulheres que fumam, ano mais recente disponível.</li><li><code>male_smokers</code>: Porcentagem de homens que fumam, ano mais recente disponível.</li><li><code>handwashing_facilities</code>: Porcentagem da população com instalações básicas de lavagem das mãos no local, ano mais recente disponível.</li><li><code>hospital_beds_per_thousand</code>: Leitos hospitalares por 1.000 pessoas, ano mais recente disponível desde 2010.</li><li><code>life_expectancy</code>: Expectativa de vida ao nascer em 2019.</li><li><code>human_development_index</code>: Um índice composto que mede a conquista média em três dimensões básicas do desenvolvimento humano - uma vida longa e saudável, conhecimento e um padrão de vida decente. Valores para 2019, importados de <a target=\"_new\" href=\"http://hdr.undp.org/en/indicators/137506\">http://hdr.undp.org/en/indicators/137506</a>.</li><li><code>excess_mortality</code>: Diferença percentual entre o número relatado de mortes semanais ou mensais em 2020-2021 e o número de mortes projetadas para o mesmo período com base nos anos anteriores. Para mais informações, consulte <a target=\"_new\" href=\"https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality\">https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality</a>.</li><li><code>excess_mortality_cumulative</code>: Diferença percentual entre o número cumulativo de mortes desde 1 de janeiro de 2020 e o número cumulativo de mortes projetadas para o mesmo período com base nos anos anteriores. Para mais informações, consulte <a target=\"_new\" href=\"https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality\">https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality</a>.</li><li><code>excess_mortality_cumulative_absolute</code>: Diferença cumulativa entre o número relatado de mortes desde 1 de janeiro de 2020 e o número de mortes projetadas para o mesmo período com base nos anos anteriores. Para mais informações, consulte <a target=\"_new\" href=\"https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality\">https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality</a>.</li><li><code>excess_mortality_cumulative_per_million</code>: Diferença cumulativa entre o número relatado de mortes desde 1 de janeiro de 2020 e o número de mortes projetadas para o mesmo período com base nos anos anteriores, por milhão de pessoas. Para mais informações, consulte <a target=\"_new\" href=\"https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality\">https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality</a>.</li></ul>\n",
        "\n",
        "<br>"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## Configuração do Ambiente & Importação de Dados"
      ],
      "metadata": {
        "id": "na1_HQuMx14H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Configurações"
      ],
      "metadata": {
        "id": "Jzv_S6Zhw040"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "# Formatação personalizada com pontos de milhar\n",
        "formatter = ticker.FuncFormatter(lambda x, _: '{:,.0f}'.format(x).replace(\",\", \".\"))\n",
        "\n",
        "\n",
        "sns.set_style()\n",
        "%matplotlib inline\n",
        "\n",
        "warnings.filterwarnings('ignore') # Ignorar Mensagens de Avisos\n",
        "\n",
        "# Configura o Pandas para não truncar as linhas e colunas ao exibir\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', 150)"
      ],
      "metadata": {
        "id": "ARJRB_5_w-FG"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importando Base"
      ],
      "metadata": {
        "id": "Ojdb6WYdvqDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ATRIBUINDO A VARIÁVEL A FONTE DE DADOS\n",
        "URL_PATH_DATA = \"https://raw.githubusercontent.com/raimota/Panorama_Covid19/main/owid-covid-data.csv\""
      ],
      "metadata": {
        "id": "OIuVsb5Nvvmt"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# LENDO A URL\n",
        "df = pd.read_csv(URL_PATH_DATA)"
      ],
      "metadata": {
        "id": "6FmRhUkMwtra"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb5By2nIMS6m"
      },
      "source": [
        "## Análise Exploratória dos Dados - EDA\n",
        "\n",
        "Na fase inicial da Análise Exploratória de Dados (AED), o objetivo é entender melhor o conjunto de dados relacionado ao COVID-19 disponibilizado pela OWID e isso será feito através de uma série de passos <br><br>\n",
        "\n",
        "1. Verificação do tamanho do DataFrame: Quantos Registros e Campos o DataFrame possui.\n",
        "\n",
        "2. Extração e impressão dos nomes dos campos: Será armazenado e imprimido os nomes das colunas do DataFrame para identificar quais variáveis estão presentes nos conjunto de dados.\n",
        "\n",
        "3. Identificação dos tipos das variáveis: Será verificado os tipos de dados de cada coluna do DataFrame para entender como os dados estão representados.\n",
        "\n",
        "4. Visualização dos primeiras e últimos registros: Será verificado os cinco primeiros e últimos registros do dataset, e uma amostra de 10 registgros para ter uma ideia inicial da sua estrutura e dos tipos de informações que ele contém.\n",
        "\n",
        "5. Identificação da porcentagem de valores nulos e ausentes: Será calculado a porcentagem de valores nulos e ausentes em cada coluna do DataFrame para avaliar a qualidade dos dados.\n",
        "\n",
        "6. Transformações de tipos: Foi convertido a coluna de data para o formato datetime para facilitar sua manipulação e análise temporal, assim como foi convertido os campos `new_cases` e `total_deaths` para a contagem que será realizada.\n",
        "\n",
        "7. Identificação dos países com mais casos e mortes na data mais atual: Será identificado os países com o maior número de casos e mortes na data mais recente disponível no conjunto de dados.\n",
        "\n",
        "8. Plotagem dos Gráficos do Item 7\n",
        "\n",
        "9. Plotagem de um gráfico de linhas para visualizar a evolução do total de casos de COVID-19 no mundo\n",
        "\n",
        "10. Plotagem de um gráfico de dispersão considerando o PIB dos países pelo número total de mortes.\n",
        "\n",
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# VERIFICANDO AS DIMENSÕES DO DATASET\n",
        "print(\n",
        "      f'Dimensões do Dataset \\n{\"-\"*25}',\n",
        "      f'\\nCampos: {df.shape[1]}',\n",
        "      f'\\nRegistros: {df.shape[0]:,.0f}'.replace(',', '.')\n",
        ")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9FxYKiN1dSp",
        "outputId": "963ceaa2-e0f0-4509-ce1e-c4769e145504"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensões do Dataset \n",
            "------------------------- \n",
            "Campos: 67 \n",
            "Registros: 387.215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ATRUIBUINDO A LISTA OS CAMPOS DO DATAFRAME\n",
        "campos_df = list(df.columns)\n",
        "campos_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9o5_Jlri3Cuk",
        "outputId": "6ce1ae45-9d2d-403d-f8ca-3488deeb8e14"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['iso_code',\n",
              " 'continent',\n",
              " 'location',\n",
              " 'date',\n",
              " 'total_cases',\n",
              " 'new_cases',\n",
              " 'new_cases_smoothed',\n",
              " 'total_deaths',\n",
              " 'new_deaths',\n",
              " 'new_deaths_smoothed',\n",
              " 'total_cases_per_million',\n",
              " 'new_cases_per_million',\n",
              " 'new_cases_smoothed_per_million',\n",
              " 'total_deaths_per_million',\n",
              " 'new_deaths_per_million',\n",
              " 'new_deaths_smoothed_per_million',\n",
              " 'reproduction_rate',\n",
              " 'icu_patients',\n",
              " 'icu_patients_per_million',\n",
              " 'hosp_patients',\n",
              " 'hosp_patients_per_million',\n",
              " 'weekly_icu_admissions',\n",
              " 'weekly_icu_admissions_per_million',\n",
              " 'weekly_hosp_admissions',\n",
              " 'weekly_hosp_admissions_per_million',\n",
              " 'total_tests',\n",
              " 'new_tests',\n",
              " 'total_tests_per_thousand',\n",
              " 'new_tests_per_thousand',\n",
              " 'new_tests_smoothed',\n",
              " 'new_tests_smoothed_per_thousand',\n",
              " 'positive_rate',\n",
              " 'tests_per_case',\n",
              " 'tests_units',\n",
              " 'total_vaccinations',\n",
              " 'people_vaccinated',\n",
              " 'people_fully_vaccinated',\n",
              " 'total_boosters',\n",
              " 'new_vaccinations',\n",
              " 'new_vaccinations_smoothed',\n",
              " 'total_vaccinations_per_hundred',\n",
              " 'people_vaccinated_per_hundred',\n",
              " 'people_fully_vaccinated_per_hundred',\n",
              " 'total_boosters_per_hundred',\n",
              " 'new_vaccinations_smoothed_per_million',\n",
              " 'new_people_vaccinated_smoothed',\n",
              " 'new_people_vaccinated_smoothed_per_hundred',\n",
              " 'stringency_index',\n",
              " 'population_density',\n",
              " 'median_age',\n",
              " 'aged_65_older',\n",
              " 'aged_70_older',\n",
              " 'gdp_per_capita',\n",
              " 'extreme_poverty',\n",
              " 'cardiovasc_death_rate',\n",
              " 'diabetes_prevalence',\n",
              " 'female_smokers',\n",
              " 'male_smokers',\n",
              " 'handwashing_facilities',\n",
              " 'hospital_beds_per_thousand',\n",
              " 'life_expectancy',\n",
              " 'human_development_index',\n",
              " 'population',\n",
              " 'excess_mortality_cumulative_absolute',\n",
              " 'excess_mortality_cumulative',\n",
              " 'excess_mortality',\n",
              " 'excess_mortality_cumulative_per_million']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# VERIFICANDO OS TIPOS DOS CAMPOS\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70xZuCTk9nFb",
        "outputId": "730abb3e-e745-4e75-8d5a-a99916da4711"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iso_code                                       object\n",
              "continent                                      object\n",
              "location                                       object\n",
              "date                                           object\n",
              "total_cases                                   float64\n",
              "new_cases                                     float64\n",
              "new_cases_smoothed                            float64\n",
              "total_deaths                                  float64\n",
              "new_deaths                                    float64\n",
              "new_deaths_smoothed                           float64\n",
              "total_cases_per_million                       float64\n",
              "new_cases_per_million                         float64\n",
              "new_cases_smoothed_per_million                float64\n",
              "total_deaths_per_million                      float64\n",
              "new_deaths_per_million                        float64\n",
              "new_deaths_smoothed_per_million               float64\n",
              "reproduction_rate                             float64\n",
              "icu_patients                                  float64\n",
              "icu_patients_per_million                      float64\n",
              "hosp_patients                                 float64\n",
              "hosp_patients_per_million                     float64\n",
              "weekly_icu_admissions                         float64\n",
              "weekly_icu_admissions_per_million             float64\n",
              "weekly_hosp_admissions                        float64\n",
              "weekly_hosp_admissions_per_million            float64\n",
              "total_tests                                   float64\n",
              "new_tests                                     float64\n",
              "total_tests_per_thousand                      float64\n",
              "new_tests_per_thousand                        float64\n",
              "new_tests_smoothed                            float64\n",
              "new_tests_smoothed_per_thousand               float64\n",
              "positive_rate                                 float64\n",
              "tests_per_case                                float64\n",
              "tests_units                                    object\n",
              "total_vaccinations                            float64\n",
              "people_vaccinated                             float64\n",
              "people_fully_vaccinated                       float64\n",
              "total_boosters                                float64\n",
              "new_vaccinations                              float64\n",
              "new_vaccinations_smoothed                     float64\n",
              "total_vaccinations_per_hundred                float64\n",
              "people_vaccinated_per_hundred                 float64\n",
              "people_fully_vaccinated_per_hundred           float64\n",
              "total_boosters_per_hundred                    float64\n",
              "new_vaccinations_smoothed_per_million         float64\n",
              "new_people_vaccinated_smoothed                float64\n",
              "new_people_vaccinated_smoothed_per_hundred    float64\n",
              "stringency_index                              float64\n",
              "population_density                            float64\n",
              "median_age                                    float64\n",
              "aged_65_older                                 float64\n",
              "aged_70_older                                 float64\n",
              "gdp_per_capita                                float64\n",
              "extreme_poverty                               float64\n",
              "cardiovasc_death_rate                         float64\n",
              "diabetes_prevalence                           float64\n",
              "female_smokers                                float64\n",
              "male_smokers                                  float64\n",
              "handwashing_facilities                        float64\n",
              "hospital_beds_per_thousand                    float64\n",
              "life_expectancy                               float64\n",
              "human_development_index                       float64\n",
              "population                                    float64\n",
              "excess_mortality_cumulative_absolute          float64\n",
              "excess_mortality_cumulative                   float64\n",
              "excess_mortality                              float64\n",
              "excess_mortality_cumulative_per_million       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Algums campos que serão utilizados não estão no formato correto mais para frente serão tratados"
      ],
      "metadata": {
        "id": "k-QmRqSecBMO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOtEJxe0DMEb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "96416e46-d8fe-4b38-8a8c-55ac77056d4a"
      },
      "source": [
        "# VISUALIZANDO OS 5 PRIMEIROS REGISTROS\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  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",
              "   total_cases_per_million  new_cases_per_million  \\\n",
              "0                      NaN                    0.0   \n",
              "1                      NaN                    0.0   \n",
              "2                      NaN                    0.0   \n",
              "3                      NaN                    0.0   \n",
              "4                      NaN                    0.0   \n",
              "\n",
              "   new_cases_smoothed_per_million  total_deaths_per_million  \\\n",
              "0                             NaN                       NaN   \n",
              "1                             NaN                       NaN   \n",
              "2                             NaN                       NaN   \n",
              "3                             NaN                       NaN   \n",
              "4                             NaN                       NaN   \n",
              "\n",
              "   new_deaths_per_million  new_deaths_smoothed_per_million  reproduction_rate  \\\n",
              "0                     0.0                              NaN                NaN   \n",
              "1                     0.0                              NaN                NaN   \n",
              "2                     0.0                              NaN                NaN   \n",
              "3                     0.0                              NaN                NaN   \n",
              "4                     0.0                              NaN                NaN   \n",
              "\n",
              "   icu_patients  icu_patients_per_million  hosp_patients  \\\n",
              "0           NaN                       NaN            NaN   \n",
              "1           NaN                       NaN            NaN   \n",
              "2           NaN                       NaN            NaN   \n",
              "3           NaN                       NaN            NaN   \n",
              "4           NaN                       NaN            NaN   \n",
              "\n",
              "   hosp_patients_per_million  weekly_icu_admissions  \\\n",
              "0                        NaN                    NaN   \n",
              "1                        NaN                    NaN   \n",
              "2                        NaN                    NaN   \n",
              "3                        NaN                    NaN   \n",
              "4                        NaN                    NaN   \n",
              "\n",
              "   weekly_icu_admissions_per_million  weekly_hosp_admissions  \\\n",
              "0                                NaN                     NaN   \n",
              "1                                NaN                     NaN   \n",
              "2                                NaN                     NaN   \n",
              "3                                NaN                     NaN   \n",
              "4                                NaN                     NaN   \n",
              "\n",
              "   weekly_hosp_admissions_per_million  total_tests  new_tests  \\\n",
              "0                                 NaN          NaN        NaN   \n",
              "1                                 NaN          NaN        NaN   \n",
              "2                                 NaN          NaN        NaN   \n",
              "3                                 NaN          NaN        NaN   \n",
              "4                                 NaN          NaN        NaN   \n",
              "\n",
              "   total_tests_per_thousand  new_tests_per_thousand  new_tests_smoothed  \\\n",
              "0                       NaN                     NaN                 NaN   \n",
              "1                       NaN                     NaN                 NaN   \n",
              "2                       NaN                     NaN                 NaN   \n",
              "3                       NaN                     NaN                 NaN   \n",
              "4                       NaN                     NaN                 NaN   \n",
              "\n",
              "   new_tests_smoothed_per_thousand  positive_rate  tests_per_case tests_units  \\\n",
              "0                              NaN            NaN             NaN         NaN   \n",
              "1                              NaN            NaN             NaN         NaN   \n",
              "2                              NaN            NaN             NaN         NaN   \n",
              "3                              NaN            NaN             NaN         NaN   \n",
              "4                              NaN            NaN             NaN         NaN   \n",
              "\n",
              "   total_vaccinations  people_vaccinated  people_fully_vaccinated  \\\n",
              "0                 NaN                NaN                      NaN   \n",
              "1                 NaN                NaN                      NaN   \n",
              "2                 NaN                NaN                      NaN   \n",
              "3                 NaN                NaN                      NaN   \n",
              "4                 NaN                NaN                      NaN   \n",
              "\n",
              "   total_boosters  new_vaccinations  new_vaccinations_smoothed  \\\n",
              "0             NaN               NaN                        NaN   \n",
              "1             NaN               NaN                        NaN   \n",
              "2             NaN               NaN                        NaN   \n",
              "3             NaN               NaN                        NaN   \n",
              "4             NaN               NaN                        NaN   \n",
              "\n",
              "   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
              "0                             NaN                            NaN   \n",
              "1                             NaN                            NaN   \n",
              "2                             NaN                            NaN   \n",
              "3                             NaN                            NaN   \n",
              "4                             NaN                            NaN   \n",
              "\n",
              "   people_fully_vaccinated_per_hundred  total_boosters_per_hundred  \\\n",
              "0                                  NaN                         NaN   \n",
              "1                                  NaN                         NaN   \n",
              "2                                  NaN                         NaN   \n",
              "3                                  NaN                         NaN   \n",
              "4                                  NaN                         NaN   \n",
              "\n",
              "   new_vaccinations_smoothed_per_million  new_people_vaccinated_smoothed  \\\n",
              "0                                    NaN                             NaN   \n",
              "1                                    NaN                             NaN   \n",
              "2                                    NaN                             NaN   \n",
              "3                                    NaN                             NaN   \n",
              "4                                    NaN                             NaN   \n",
              "\n",
              "   new_people_vaccinated_smoothed_per_hundred  stringency_index  \\\n",
              "0                                         NaN               0.0   \n",
              "1                                         NaN               0.0   \n",
              "2                                         NaN               0.0   \n",
              "3                                         NaN               0.0   \n",
              "4                                         NaN               0.0   \n",
              "\n",
              "   population_density  median_age  aged_65_older  aged_70_older  \\\n",
              "0              54.422        18.6          2.581          1.337   \n",
              "1              54.422        18.6          2.581          1.337   \n",
              "2              54.422        18.6          2.581          1.337   \n",
              "3              54.422        18.6          2.581          1.337   \n",
              "4              54.422        18.6          2.581          1.337   \n",
              "\n",
              "   gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "0        1803.987              NaN                597.029   \n",
              "1        1803.987              NaN                597.029   \n",
              "2        1803.987              NaN                597.029   \n",
              "3        1803.987              NaN                597.029   \n",
              "4        1803.987              NaN                597.029   \n",
              "\n",
              "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
              "0                 9.59             NaN           NaN                  37.746   \n",
              "1                 9.59             NaN           NaN                  37.746   \n",
              "2                 9.59             NaN           NaN                  37.746   \n",
              "3                 9.59             NaN           NaN                  37.746   \n",
              "4                 9.59             NaN           NaN                  37.746   \n",
              "\n",
              "   hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
              "0                         0.5            64.83                    0.511   \n",
              "1                         0.5            64.83                    0.511   \n",
              "2                         0.5            64.83                    0.511   \n",
              "3                         0.5            64.83                    0.511   \n",
              "4                         0.5            64.83                    0.511   \n",
              "\n",
              "   population  excess_mortality_cumulative_absolute  \\\n",
              "0  41128772.0                                   NaN   \n",
              "1  41128772.0                                   NaN   \n",
              "2  41128772.0                                   NaN   \n",
              "3  41128772.0                                   NaN   \n",
              "4  41128772.0                                   NaN   \n",
              "\n",
              "   excess_mortality_cumulative  excess_mortality  \\\n",
              "0                          NaN               NaN   \n",
              "1                          NaN               NaN   \n",
              "2                          NaN               NaN   \n",
              "3                          NaN               NaN   \n",
              "4                          NaN               NaN   \n",
              "\n",
              "   excess_mortality_cumulative_per_million  \n",
              "0                                      NaN  \n",
              "1                                      NaN  \n",
              "2                                      NaN  \n",
              "3                                      NaN  \n",
              "4                                      NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-daa8cd94-9df8-4a8f-a894-46acd5cf11be\" 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>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>total_boosters</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>total_boosters_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>new_people_vaccinated_smoothed</th>\n",
              "      <th>new_people_vaccinated_smoothed_per_hundred</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "      <th>population</th>\n",
              "      <th>excess_mortality_cumulative_absolute</th>\n",
              "      <th>excess_mortality_cumulative</th>\n",
              "      <th>excess_mortality</th>\n",
              "      <th>excess_mortality_cumulative_per_million</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "      <td>41128772.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "      <td>41128772.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "      <td>41128772.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "      <td>41128772.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "      <td>41128772.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daa8cd94-9df8-4a8f-a894-46acd5cf11be')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-daa8cd94-9df8-4a8f-a894-46acd5cf11be 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-daa8cd94-9df8-4a8f-a894-46acd5cf11be');\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",
              "\n",
              "\n",
              "<div id=\"df-14f86496-e9cd-4eb2-858c-5959efe4248d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14f86496-e9cd-4eb2-858c-5959efe4248d')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14f86496-e9cd-4eb2-858c-5959efe4248d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# VISUALIZANDO OS ÚLTIMOS 5 REGISTROS\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "25UhvpKf1AMK",
        "outputId": "61237b58-077c-4d9a-fbf9-ccb4a593dc89"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       iso_code continent  location        date  total_cases  new_cases  \\\n",
              "387210      ZWE    Africa  Zimbabwe  2024-03-13     266359.0        0.0   \n",
              "387211      ZWE    Africa  Zimbabwe  2024-03-14     266359.0        0.0   \n",
              "387212      ZWE    Africa  Zimbabwe  2024-03-15     266359.0        0.0   \n",
              "387213      ZWE    Africa  Zimbabwe  2024-03-16     266359.0        0.0   \n",
              "387214      ZWE    Africa  Zimbabwe  2024-03-17     266359.0        0.0   \n",
              "\n",
              "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
              "387210               0.143        5740.0         0.0                  0.0   \n",
              "387211               0.143        5740.0         0.0                  0.0   \n",
              "387212               0.143        5740.0         0.0                  0.0   \n",
              "387213               0.143        5740.0         0.0                  0.0   \n",
              "387214               0.000        5740.0         0.0                  0.0   \n",
              "\n",
              "        total_cases_per_million  new_cases_per_million  \\\n",
              "387210                16320.478                    0.0   \n",
              "387211                16320.478                    0.0   \n",
              "387212                16320.478                    0.0   \n",
              "387213                16320.478                    0.0   \n",
              "387214                16320.478                    0.0   \n",
              "\n",
              "        new_cases_smoothed_per_million  total_deaths_per_million  \\\n",
              "387210                           0.009                   351.704   \n",
              "387211                           0.009                   351.704   \n",
              "387212                           0.009                   351.704   \n",
              "387213                           0.009                   351.704   \n",
              "387214                           0.000                   351.704   \n",
              "\n",
              "        new_deaths_per_million  new_deaths_smoothed_per_million  \\\n",
              "387210                     0.0                              0.0   \n",
              "387211                     0.0                              0.0   \n",
              "387212                     0.0                              0.0   \n",
              "387213                     0.0                              0.0   \n",
              "387214                     0.0                              0.0   \n",
              "\n",
              "        reproduction_rate  icu_patients  icu_patients_per_million  \\\n",
              "387210                NaN           NaN                       NaN   \n",
              "387211                NaN           NaN                       NaN   \n",
              "387212                NaN           NaN                       NaN   \n",
              "387213                NaN           NaN                       NaN   \n",
              "387214                NaN           NaN                       NaN   \n",
              "\n",
              "        hosp_patients  hosp_patients_per_million  weekly_icu_admissions  \\\n",
              "387210            NaN                        NaN                    NaN   \n",
              "387211            NaN                        NaN                    NaN   \n",
              "387212            NaN                        NaN                    NaN   \n",
              "387213            NaN                        NaN                    NaN   \n",
              "387214            NaN                        NaN                    NaN   \n",
              "\n",
              "        weekly_icu_admissions_per_million  weekly_hosp_admissions  \\\n",
              "387210                                NaN                     NaN   \n",
              "387211                                NaN                     NaN   \n",
              "387212                                NaN                     NaN   \n",
              "387213                                NaN                     NaN   \n",
              "387214                                NaN                     NaN   \n",
              "\n",
              "        weekly_hosp_admissions_per_million  total_tests  new_tests  \\\n",
              "387210                                 NaN          NaN        NaN   \n",
              "387211                                 NaN          NaN        NaN   \n",
              "387212                                 NaN          NaN        NaN   \n",
              "387213                                 NaN          NaN        NaN   \n",
              "387214                                 NaN          NaN        NaN   \n",
              "\n",
              "        total_tests_per_thousand  new_tests_per_thousand  new_tests_smoothed  \\\n",
              "387210                       NaN                     NaN                 NaN   \n",
              "387211                       NaN                     NaN                 NaN   \n",
              "387212                       NaN                     NaN                 NaN   \n",
              "387213                       NaN                     NaN                 NaN   \n",
              "387214                       NaN                     NaN                 NaN   \n",
              "\n",
              "        new_tests_smoothed_per_thousand  positive_rate  tests_per_case  \\\n",
              "387210                              NaN            NaN             NaN   \n",
              "387211                              NaN            NaN             NaN   \n",
              "387212                              NaN            NaN             NaN   \n",
              "387213                              NaN            NaN             NaN   \n",
              "387214                              NaN            NaN             NaN   \n",
              "\n",
              "       tests_units  total_vaccinations  people_vaccinated  \\\n",
              "387210         NaN                 NaN                NaN   \n",
              "387211         NaN                 NaN                NaN   \n",
              "387212         NaN                 NaN                NaN   \n",
              "387213         NaN                 NaN                NaN   \n",
              "387214         NaN                 NaN                NaN   \n",
              "\n",
              "        people_fully_vaccinated  total_boosters  new_vaccinations  \\\n",
              "387210                      NaN             NaN               NaN   \n",
              "387211                      NaN             NaN               NaN   \n",
              "387212                      NaN             NaN               NaN   \n",
              "387213                      NaN             NaN               NaN   \n",
              "387214                      NaN             NaN               NaN   \n",
              "\n",
              "        new_vaccinations_smoothed  total_vaccinations_per_hundred  \\\n",
              "387210                        NaN                             NaN   \n",
              "387211                        NaN                             NaN   \n",
              "387212                        NaN                             NaN   \n",
              "387213                        NaN                             NaN   \n",
              "387214                        NaN                             NaN   \n",
              "\n",
              "        people_vaccinated_per_hundred  people_fully_vaccinated_per_hundred  \\\n",
              "387210                            NaN                                  NaN   \n",
              "387211                            NaN                                  NaN   \n",
              "387212                            NaN                                  NaN   \n",
              "387213                            NaN                                  NaN   \n",
              "387214                            NaN                                  NaN   \n",
              "\n",
              "        total_boosters_per_hundred  new_vaccinations_smoothed_per_million  \\\n",
              "387210                         NaN                                    NaN   \n",
              "387211                         NaN                                    NaN   \n",
              "387212                         NaN                                    NaN   \n",
              "387213                         NaN                                    NaN   \n",
              "387214                         NaN                                    NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed  \\\n",
              "387210                             NaN   \n",
              "387211                             NaN   \n",
              "387212                             NaN   \n",
              "387213                             NaN   \n",
              "387214                             NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed_per_hundred  stringency_index  \\\n",
              "387210                                         NaN               NaN   \n",
              "387211                                         NaN               NaN   \n",
              "387212                                         NaN               NaN   \n",
              "387213                                         NaN               NaN   \n",
              "387214                                         NaN               NaN   \n",
              "\n",
              "        population_density  median_age  aged_65_older  aged_70_older  \\\n",
              "387210              42.729        19.6          2.822          1.882   \n",
              "387211              42.729        19.6          2.822          1.882   \n",
              "387212              42.729        19.6          2.822          1.882   \n",
              "387213              42.729        19.6          2.822          1.882   \n",
              "387214              42.729        19.6          2.822          1.882   \n",
              "\n",
              "        gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "387210        1899.775             21.4                307.846   \n",
              "387211        1899.775             21.4                307.846   \n",
              "387212        1899.775             21.4                307.846   \n",
              "387213        1899.775             21.4                307.846   \n",
              "387214        1899.775             21.4                307.846   \n",
              "\n",
              "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "387210                 1.82             1.6          30.7   \n",
              "387211                 1.82             1.6          30.7   \n",
              "387212                 1.82             1.6          30.7   \n",
              "387213                 1.82             1.6          30.7   \n",
              "387214                 1.82             1.6          30.7   \n",
              "\n",
              "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "387210                  36.791                         1.7            61.49   \n",
              "387211                  36.791                         1.7            61.49   \n",
              "387212                  36.791                         1.7            61.49   \n",
              "387213                  36.791                         1.7            61.49   \n",
              "387214                  36.791                         1.7            61.49   \n",
              "\n",
              "        human_development_index  population  \\\n",
              "387210                    0.571  16320539.0   \n",
              "387211                    0.571  16320539.0   \n",
              "387212                    0.571  16320539.0   \n",
              "387213                    0.571  16320539.0   \n",
              "387214                    0.571  16320539.0   \n",
              "\n",
              "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
              "387210                                   NaN                          NaN   \n",
              "387211                                   NaN                          NaN   \n",
              "387212                                   NaN                          NaN   \n",
              "387213                                   NaN                          NaN   \n",
              "387214                                   NaN                          NaN   \n",
              "\n",
              "        excess_mortality  excess_mortality_cumulative_per_million  \n",
              "387210               NaN                                      NaN  \n",
              "387211               NaN                                      NaN  \n",
              "387212               NaN                                      NaN  \n",
              "387213               NaN                                      NaN  \n",
              "387214               NaN                                      NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b90af84-2110-4ae6-be3a-13e7368314d2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>total_boosters</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>total_boosters_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>new_people_vaccinated_smoothed</th>\n",
              "      <th>new_people_vaccinated_smoothed_per_hundred</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "      <th>population</th>\n",
              "      <th>excess_mortality_cumulative_absolute</th>\n",
              "      <th>excess_mortality_cumulative</th>\n",
              "      <th>excess_mortality</th>\n",
              "      <th>excess_mortality_cumulative_per_million</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>387210</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2024-03-13</td>\n",
              "      <td>266359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>5740.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16320.478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009</td>\n",
              "      <td>351.704</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "      <td>16320539.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387211</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2024-03-14</td>\n",
              "      <td>266359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>5740.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16320.478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009</td>\n",
              "      <td>351.704</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "      <td>16320539.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387212</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2024-03-15</td>\n",
              "      <td>266359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>5740.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16320.478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009</td>\n",
              "      <td>351.704</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "      <td>16320539.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387213</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2024-03-16</td>\n",
              "      <td>266359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>5740.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16320.478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009</td>\n",
              "      <td>351.704</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "      <td>16320539.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387214</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>266359.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5740.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16320.478</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>351.704</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "      <td>16320539.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b90af84-2110-4ae6-be3a-13e7368314d2')\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\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",
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              "    .colab-df-buttons div {\n",
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              "    [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-4b90af84-2110-4ae6-be3a-13e7368314d2 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-4b90af84-2110-4ae6-be3a-13e7368314d2');\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",
              "\n",
              "\n",
              "<div id=\"df-6d67bc0d-91df-4ad5-a4b3-1599119e8f4b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d67bc0d-91df-4ad5-a4b3-1599119e8f4b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6d67bc0d-91df-4ad5-a4b3-1599119e8f4b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# VISUALIZANDO 10 AMOSTRAS ALEATÓRIAS DO DATASET\n",
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "J89gExqG1Mo-",
        "outputId": "d94da4c0-3380-4b00-a0e3-dea1e0dd8052"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        iso_code      continent               location        date  \\\n",
              "257947  OWID_OCE            NaN                Oceania  2021-02-27   \n",
              "349200       TTO  North America    Trinidad and Tobago  2020-02-04   \n",
              "34170        BLZ  North America                 Belize  2021-01-31   \n",
              "167798       JAM  North America                Jamaica  2021-10-02   \n",
              "314577       SVK         Europe               Slovakia  2021-06-03   \n",
              "245296       NGA         Africa                Nigeria  2021-08-06   \n",
              "383392       YEM           Asia                  Yemen  2022-02-22   \n",
              "150169       HND  North America               Honduras  2023-11-22   \n",
              "111838       FRO         Europe         Faeroe Islands  2024-01-04   \n",
              "300719       STP         Africa  Sao Tome and Principe  2020-08-26   \n",
              "\n",
              "        total_cases  new_cases  new_cases_smoothed  total_deaths  new_deaths  \\\n",
              "257947      58128.0        0.0              43.143        1230.0         0.0   \n",
              "349200          NaN        0.0               0.000           NaN         0.0   \n",
              "34170       11877.0      177.0              25.286         301.0        11.0   \n",
              "167798      82349.0        0.0             460.286        1824.0         0.0   \n",
              "314577     389690.0        0.0             122.143       12339.0         0.0   \n",
              "245296     173908.0        0.0             430.429        2149.0         0.0   \n",
              "383392      11731.0        0.0               9.571        2120.0         0.0   \n",
              "150169     472743.0        0.0               0.000       11114.0         0.0   \n",
              "111838      34658.0        0.0               0.000          28.0         0.0   \n",
              "300719        892.0        0.0               1.000          15.0         0.0   \n",
              "\n",
              "        new_deaths_smoothed  total_cases_per_million  new_cases_per_million  \\\n",
              "257947                0.571                 1290.619                   0.00   \n",
              "349200                0.000                      NaN                   0.00   \n",
              "34170                 1.571                29305.304                 436.73   \n",
              "167798                6.714                29125.530                   0.00   \n",
              "314577                6.714                69051.671                   0.00   \n",
              "245296                2.429                  795.768                   0.00   \n",
              "383392                2.571                  348.136                   0.00   \n",
              "150169                0.000                45312.895                   0.00   \n",
              "111838                0.000               652484.139                   0.00   \n",
              "300719                0.000                 3922.724                   0.00   \n",
              "\n",
              "        new_cases_smoothed_per_million  total_deaths_per_million  \\\n",
              "257947                           0.958                    27.310   \n",
              "349200                           0.000                       NaN   \n",
              "34170                           62.390                   742.687   \n",
              "167798                         162.796                   645.120   \n",
              "314577                          21.643                  2186.427   \n",
              "245296                           1.970                     9.833   \n",
              "383392                           0.284                    62.914   \n",
              "150169                           0.000                  1065.288   \n",
              "111838                           0.000                   527.138   \n",
              "300719                           4.398                    65.965   \n",
              "\n",
              "        new_deaths_per_million  new_deaths_smoothed_per_million  \\\n",
              "257947                   0.000                            0.013   \n",
              "349200                   0.000                            0.000   \n",
              "34170                   27.141                            3.877   \n",
              "167798                   0.000                            2.375   \n",
              "314577                   0.000                            1.190   \n",
              "245296                   0.000                            0.011   \n",
              "383392                   0.000                            0.076   \n",
              "150169                   0.000                            0.000   \n",
              "111838                   0.000                            0.000   \n",
              "300719                   0.000                            0.000   \n",
              "\n",
              "        reproduction_rate  icu_patients  icu_patients_per_million  \\\n",
              "257947                NaN           NaN                       NaN   \n",
              "349200                NaN           NaN                       NaN   \n",
              "34170                0.78           NaN                       NaN   \n",
              "167798               0.75           NaN                       NaN   \n",
              "314577               0.71          83.0                    14.707   \n",
              "245296               1.26           NaN                       NaN   \n",
              "383392               0.53           NaN                       NaN   \n",
              "150169                NaN           NaN                       NaN   \n",
              "111838                NaN           NaN                       NaN   \n",
              "300719               0.66           NaN                       NaN   \n",
              "\n",
              "        hosp_patients  hosp_patients_per_million  weekly_icu_admissions  \\\n",
              "257947            NaN                        NaN                    NaN   \n",
              "349200            NaN                        NaN                    NaN   \n",
              "34170             NaN                        NaN                    NaN   \n",
              "167798            NaN                        NaN                    NaN   \n",
              "314577          318.0                     56.348                    NaN   \n",
              "245296            NaN                        NaN                    NaN   \n",
              "383392            NaN                        NaN                    NaN   \n",
              "150169            NaN                        NaN                    NaN   \n",
              "111838            NaN                        NaN                    NaN   \n",
              "300719            NaN                        NaN                    NaN   \n",
              "\n",
              "        weekly_icu_admissions_per_million  weekly_hosp_admissions  \\\n",
              "257947                                NaN                     NaN   \n",
              "349200                                NaN                     NaN   \n",
              "34170                                 NaN                     NaN   \n",
              "167798                                NaN                     NaN   \n",
              "314577                                NaN                     NaN   \n",
              "245296                                NaN                     NaN   \n",
              "383392                                NaN                     NaN   \n",
              "150169                                NaN                     NaN   \n",
              "111838                                NaN                     NaN   \n",
              "300719                                NaN                     NaN   \n",
              "\n",
              "        weekly_hosp_admissions_per_million  total_tests  new_tests  \\\n",
              "257947                                 NaN          NaN        NaN   \n",
              "349200                                 NaN          NaN        NaN   \n",
              "34170                                  NaN          NaN        NaN   \n",
              "167798                                 NaN          NaN        NaN   \n",
              "314577                                 NaN   38683563.0    55825.0   \n",
              "245296                                 NaN          NaN        NaN   \n",
              "383392                                 NaN          NaN        NaN   \n",
              "150169                                 NaN          NaN        NaN   \n",
              "111838                                 NaN          NaN        NaN   \n",
              "300719                                 NaN          NaN        NaN   \n",
              "\n",
              "        total_tests_per_thousand  new_tests_per_thousand  new_tests_smoothed  \\\n",
              "257947                       NaN                     NaN                 NaN   \n",
              "349200                       NaN                     NaN                 NaN   \n",
              "34170                        NaN                     NaN                 NaN   \n",
              "167798                       NaN                     NaN              1467.0   \n",
              "314577                    7101.0                  10.248             73036.0   \n",
              "245296                       NaN                     NaN             11708.0   \n",
              "383392                       NaN                     NaN                 NaN   \n",
              "150169                       NaN                     NaN                 NaN   \n",
              "111838                       NaN                     NaN                 NaN   \n",
              "300719                       NaN                     NaN                 NaN   \n",
              "\n",
              "        new_tests_smoothed_per_thousand  positive_rate  tests_per_case  \\\n",
              "257947                              NaN            NaN             NaN   \n",
              "349200                              NaN            NaN             NaN   \n",
              "34170                               NaN            NaN             NaN   \n",
              "167798                            0.519         0.2014             5.0   \n",
              "314577                           13.407         0.0040           250.0   \n",
              "245296                            0.055         0.0455            22.0   \n",
              "383392                              NaN            NaN             NaN   \n",
              "150169                              NaN            NaN             NaN   \n",
              "111838                              NaN            NaN             NaN   \n",
              "300719                              NaN            NaN             NaN   \n",
              "\n",
              "            tests_units  total_vaccinations  people_vaccinated  \\\n",
              "257947              NaN             50234.0            47081.0   \n",
              "349200              NaN                 NaN                NaN   \n",
              "34170               NaN                 NaN                NaN   \n",
              "167798   samples tested            788984.0           537510.0   \n",
              "314577  tests performed                 NaN                NaN   \n",
              "245296  tests performed                 NaN                NaN   \n",
              "383392              NaN                 NaN                NaN   \n",
              "150169              NaN                 NaN                NaN   \n",
              "111838              NaN                 NaN                NaN   \n",
              "300719              NaN                 NaN                NaN   \n",
              "\n",
              "        people_fully_vaccinated  total_boosters  new_vaccinations  \\\n",
              "257947                   3153.0             NaN            3888.0   \n",
              "349200                      NaN             NaN               NaN   \n",
              "34170                       NaN             NaN               NaN   \n",
              "167798                 280843.0             NaN               NaN   \n",
              "314577                      NaN             NaN               NaN   \n",
              "245296                      NaN             NaN               NaN   \n",
              "383392                      NaN             NaN               NaN   \n",
              "150169                      NaN             NaN               NaN   \n",
              "111838                      NaN             NaN               NaN   \n",
              "300719                      NaN             NaN               NaN   \n",
              "\n",
              "        new_vaccinations_smoothed  total_vaccinations_per_hundred  \\\n",
              "257947                     6747.0                            0.11   \n",
              "349200                        NaN                             NaN   \n",
              "34170                         NaN                             NaN   \n",
              "167798                     9145.0                           27.91   \n",
              "314577                    34748.0                             NaN   \n",
              "245296                     1002.0                             NaN   \n",
              "383392                      833.0                             NaN   \n",
              "150169                        NaN                             NaN   \n",
              "111838                        NaN                             NaN   \n",
              "300719                        NaN                             NaN   \n",
              "\n",
              "        people_vaccinated_per_hundred  people_fully_vaccinated_per_hundred  \\\n",
              "257947                           0.10                                 0.01   \n",
              "349200                            NaN                                  NaN   \n",
              "34170                             NaN                                  NaN   \n",
              "167798                          19.01                                 9.93   \n",
              "314577                            NaN                                  NaN   \n",
              "245296                            NaN                                  NaN   \n",
              "383392                            NaN                                  NaN   \n",
              "150169                            NaN                                  NaN   \n",
              "111838                            NaN                                  NaN   \n",
              "300719                            NaN                                  NaN   \n",
              "\n",
              "        total_boosters_per_hundred  new_vaccinations_smoothed_per_million  \\\n",
              "257947                         NaN                                  150.0   \n",
              "349200                         NaN                                    NaN   \n",
              "34170                          NaN                                    NaN   \n",
              "167798                         NaN                                 3234.0   \n",
              "314577                         NaN                                 6157.0   \n",
              "245296                         NaN                                    5.0   \n",
              "383392                         NaN                                   25.0   \n",
              "150169                         NaN                                    NaN   \n",
              "111838                         NaN                                    NaN   \n",
              "300719                         NaN                                    NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed  \\\n",
              "257947                          6488.0   \n",
              "349200                             NaN   \n",
              "34170                              NaN   \n",
              "167798                          2337.0   \n",
              "314577                         11981.0   \n",
              "245296                           578.0   \n",
              "383392                           747.0   \n",
              "150169                             NaN   \n",
              "111838                             NaN   \n",
              "300719                             NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed_per_hundred  stringency_index  \\\n",
              "257947                                       0.014               NaN   \n",
              "349200                                         NaN             13.89   \n",
              "34170                                          NaN             73.15   \n",
              "167798                                       0.083             83.33   \n",
              "314577                                       0.212             51.20   \n",
              "245296                                       0.000             45.37   \n",
              "383392                                       0.002             26.85   \n",
              "150169                                         NaN               NaN   \n",
              "111838                                         NaN               NaN   \n",
              "300719                                         NaN               NaN   \n",
              "\n",
              "        population_density  median_age  aged_65_older  aged_70_older  \\\n",
              "257947                 NaN         NaN            NaN            NaN   \n",
              "349200             266.886        36.2         10.014          5.819   \n",
              "34170               16.426        25.0          3.853          2.279   \n",
              "167798             266.879        31.4          9.684          6.390   \n",
              "314577             113.128        41.2         15.070          9.167   \n",
              "245296             209.588        18.1          2.751          1.447   \n",
              "383392              53.508        20.3          2.922          1.583   \n",
              "150169              82.805        24.9          4.652          2.883   \n",
              "111838              35.308         NaN            NaN            NaN   \n",
              "300719             212.841        18.7          2.886          2.162   \n",
              "\n",
              "        gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "257947             NaN              NaN                    NaN   \n",
              "349200       28763.071              NaN                228.467   \n",
              "34170         7824.362              NaN                176.957   \n",
              "167798        8193.571              NaN                206.537   \n",
              "314577       30155.152              0.7                287.959   \n",
              "245296        5338.454              NaN                181.013   \n",
              "383392        1479.147             18.8                495.003   \n",
              "150169        4541.795             16.0                240.208   \n",
              "111838             NaN              NaN                    NaN   \n",
              "300719        3052.714             32.3                270.113   \n",
              "\n",
              "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "257947                  NaN             NaN           NaN   \n",
              "349200                10.97             NaN           NaN   \n",
              "34170                 17.11             NaN           NaN   \n",
              "167798                11.28             5.3          28.6   \n",
              "314577                 7.29            23.1          37.7   \n",
              "245296                 2.42             0.6          10.8   \n",
              "383392                 5.35             7.6          29.2   \n",
              "150169                 7.21             2.0           NaN   \n",
              "111838                  NaN             NaN           NaN   \n",
              "300719                 2.42             NaN           NaN   \n",
              "\n",
              "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "257947                     NaN                         NaN              NaN   \n",
              "349200                  89.443                        3.00            73.51   \n",
              "34170                   90.083                        1.30            74.62   \n",
              "167798                  66.425                        1.70            74.47   \n",
              "314577                     NaN                        5.82            77.54   \n",
              "245296                  41.949                         NaN            54.69   \n",
              "383392                  49.542                        0.70            66.12   \n",
              "150169                  84.169                        0.70            75.27   \n",
              "111838                     NaN                         NaN            80.67   \n",
              "300719                  41.340                        2.90            70.39   \n",
              "\n",
              "        human_development_index   population  \\\n",
              "257947                      NaN   45038860.0   \n",
              "349200                    0.796    1531043.0   \n",
              "34170                     0.716     405285.0   \n",
              "167798                    0.734    2827382.0   \n",
              "314577                    0.860    5643455.0   \n",
              "245296                    0.539  218541216.0   \n",
              "383392                    0.470   33696612.0   \n",
              "150169                    0.634   10432858.0   \n",
              "111838                      NaN      53117.0   \n",
              "300719                    0.625     227393.0   \n",
              "\n",
              "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
              "257947                                   NaN                          NaN   \n",
              "349200                                   NaN                          NaN   \n",
              "34170                              144.99998                         6.56   \n",
              "167798                                   NaN                          NaN   \n",
              "314577                                   NaN                          NaN   \n",
              "245296                                   NaN                          NaN   \n",
              "383392                                   NaN                          NaN   \n",
              "150169                                   NaN                          NaN   \n",
              "111838                                   NaN                          NaN   \n",
              "300719                                   NaN                          NaN   \n",
              "\n",
              "        excess_mortality  excess_mortality_cumulative_per_million  \n",
              "257947               NaN                                      NaN  \n",
              "349200               NaN                                      NaN  \n",
              "34170               9.63                                362.46643  \n",
              "167798               NaN                                      NaN  \n",
              "314577               NaN                                      NaN  \n",
              "245296               NaN                                      NaN  \n",
              "383392               NaN                                      NaN  \n",
              "150169               NaN                                      NaN  \n",
              "111838               NaN                                      NaN  \n",
              "300719               NaN                                      NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d86c8896-9ed1-4aa1-b113-797c7caf5fe9\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>total_boosters</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>total_boosters_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>new_people_vaccinated_smoothed</th>\n",
              "      <th>new_people_vaccinated_smoothed_per_hundred</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "      <th>population</th>\n",
              "      <th>excess_mortality_cumulative_absolute</th>\n",
              "      <th>excess_mortality_cumulative</th>\n",
              "      <th>excess_mortality</th>\n",
              "      <th>excess_mortality_cumulative_per_million</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>257947</th>\n",
              "      <td>OWID_OCE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>2021-02-27</td>\n",
              "      <td>58128.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.143</td>\n",
              "      <td>1230.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571</td>\n",
              "      <td>1290.619</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.958</td>\n",
              "      <td>27.310</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.013</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50234.0</td>\n",
              "      <td>47081.0</td>\n",
              "      <td>3153.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3888.0</td>\n",
              "      <td>6747.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>6488.0</td>\n",
              "      <td>0.014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45038860.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349200</th>\n",
              "      <td>TTO</td>\n",
              "      <td>North America</td>\n",
              "      <td>Trinidad and Tobago</td>\n",
              "      <td>2020-02-04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.89</td>\n",
              "      <td>266.886</td>\n",
              "      <td>36.2</td>\n",
              "      <td>10.014</td>\n",
              "      <td>5.819</td>\n",
              "      <td>28763.071</td>\n",
              "      <td>NaN</td>\n",
              "      <td>228.467</td>\n",
              "      <td>10.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.443</td>\n",
              "      <td>3.00</td>\n",
              "      <td>73.51</td>\n",
              "      <td>0.796</td>\n",
              "      <td>1531043.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34170</th>\n",
              "      <td>BLZ</td>\n",
              "      <td>North America</td>\n",
              "      <td>Belize</td>\n",
              "      <td>2021-01-31</td>\n",
              "      <td>11877.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>25.286</td>\n",
              "      <td>301.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.571</td>\n",
              "      <td>29305.304</td>\n",
              "      <td>436.73</td>\n",
              "      <td>62.390</td>\n",
              "      <td>742.687</td>\n",
              "      <td>27.141</td>\n",
              "      <td>3.877</td>\n",
              "      <td>0.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>73.15</td>\n",
              "      <td>16.426</td>\n",
              "      <td>25.0</td>\n",
              "      <td>3.853</td>\n",
              "      <td>2.279</td>\n",
              "      <td>7824.362</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.957</td>\n",
              "      <td>17.11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.083</td>\n",
              "      <td>1.30</td>\n",
              "      <td>74.62</td>\n",
              "      <td>0.716</td>\n",
              "      <td>405285.0</td>\n",
              "      <td>144.99998</td>\n",
              "      <td>6.56</td>\n",
              "      <td>9.63</td>\n",
              "      <td>362.46643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167798</th>\n",
              "      <td>JAM</td>\n",
              "      <td>North America</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>2021-10-02</td>\n",
              "      <td>82349.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>460.286</td>\n",
              "      <td>1824.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.714</td>\n",
              "      <td>29125.530</td>\n",
              "      <td>0.00</td>\n",
              "      <td>162.796</td>\n",
              "      <td>645.120</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.375</td>\n",
              "      <td>0.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>0.519</td>\n",
              "      <td>0.2014</td>\n",
              "      <td>5.0</td>\n",
              "      <td>samples tested</td>\n",
              "      <td>788984.0</td>\n",
              "      <td>537510.0</td>\n",
              "      <td>280843.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9145.0</td>\n",
              "      <td>27.91</td>\n",
              "      <td>19.01</td>\n",
              "      <td>9.93</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3234.0</td>\n",
              "      <td>2337.0</td>\n",
              "      <td>0.083</td>\n",
              "      <td>83.33</td>\n",
              "      <td>266.879</td>\n",
              "      <td>31.4</td>\n",
              "      <td>9.684</td>\n",
              "      <td>6.390</td>\n",
              "      <td>8193.571</td>\n",
              "      <td>NaN</td>\n",
              "      <td>206.537</td>\n",
              "      <td>11.28</td>\n",
              "      <td>5.3</td>\n",
              "      <td>28.6</td>\n",
              "      <td>66.425</td>\n",
              "      <td>1.70</td>\n",
              "      <td>74.47</td>\n",
              "      <td>0.734</td>\n",
              "      <td>2827382.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314577</th>\n",
              "      <td>SVK</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Slovakia</td>\n",
              "      <td>2021-06-03</td>\n",
              "      <td>389690.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122.143</td>\n",
              "      <td>12339.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.714</td>\n",
              "      <td>69051.671</td>\n",
              "      <td>0.00</td>\n",
              "      <td>21.643</td>\n",
              "      <td>2186.427</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.190</td>\n",
              "      <td>0.71</td>\n",
              "      <td>83.0</td>\n",
              "      <td>14.707</td>\n",
              "      <td>318.0</td>\n",
              "      <td>56.348</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38683563.0</td>\n",
              "      <td>55825.0</td>\n",
              "      <td>7101.0</td>\n",
              "      <td>10.248</td>\n",
              "      <td>73036.0</td>\n",
              "      <td>13.407</td>\n",
              "      <td>0.0040</td>\n",
              "      <td>250.0</td>\n",
              "      <td>tests performed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34748.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6157.0</td>\n",
              "      <td>11981.0</td>\n",
              "      <td>0.212</td>\n",
              "      <td>51.20</td>\n",
              "      <td>113.128</td>\n",
              "      <td>41.2</td>\n",
              "      <td>15.070</td>\n",
              "      <td>9.167</td>\n",
              "      <td>30155.152</td>\n",
              "      <td>0.7</td>\n",
              "      <td>287.959</td>\n",
              "      <td>7.29</td>\n",
              "      <td>23.1</td>\n",
              "      <td>37.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.82</td>\n",
              "      <td>77.54</td>\n",
              "      <td>0.860</td>\n",
              "      <td>5643455.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245296</th>\n",
              "      <td>NGA</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>2021-08-06</td>\n",
              "      <td>173908.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>430.429</td>\n",
              "      <td>2149.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.429</td>\n",
              "      <td>795.768</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.970</td>\n",
              "      <td>9.833</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.011</td>\n",
              "      <td>1.26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11708.0</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.0455</td>\n",
              "      <td>22.0</td>\n",
              "      <td>tests performed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1002.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>578.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>45.37</td>\n",
              "      <td>209.588</td>\n",
              "      <td>18.1</td>\n",
              "      <td>2.751</td>\n",
              "      <td>1.447</td>\n",
              "      <td>5338.454</td>\n",
              "      <td>NaN</td>\n",
              "      <td>181.013</td>\n",
              "      <td>2.42</td>\n",
              "      <td>0.6</td>\n",
              "      <td>10.8</td>\n",
              "      <td>41.949</td>\n",
              "      <td>NaN</td>\n",
              "      <td>54.69</td>\n",
              "      <td>0.539</td>\n",
              "      <td>218541216.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383392</th>\n",
              "      <td>YEM</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>2022-02-22</td>\n",
              "      <td>11731.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.571</td>\n",
              "      <td>2120.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.571</td>\n",
              "      <td>348.136</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.284</td>\n",
              "      <td>62.914</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.076</td>\n",
              "      <td>0.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>833.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>747.0</td>\n",
              "      <td>0.002</td>\n",
              "      <td>26.85</td>\n",
              "      <td>53.508</td>\n",
              "      <td>20.3</td>\n",
              "      <td>2.922</td>\n",
              "      <td>1.583</td>\n",
              "      <td>1479.147</td>\n",
              "      <td>18.8</td>\n",
              "      <td>495.003</td>\n",
              "      <td>5.35</td>\n",
              "      <td>7.6</td>\n",
              "      <td>29.2</td>\n",
              "      <td>49.542</td>\n",
              "      <td>0.70</td>\n",
              "      <td>66.12</td>\n",
              "      <td>0.470</td>\n",
              "      <td>33696612.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150169</th>\n",
              "      <td>HND</td>\n",
              "      <td>North America</td>\n",
              "      <td>Honduras</td>\n",
              "      <td>2023-11-22</td>\n",
              "      <td>472743.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>11114.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>45312.895</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1065.288</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>82.805</td>\n",
              "      <td>24.9</td>\n",
              "      <td>4.652</td>\n",
              "      <td>2.883</td>\n",
              "      <td>4541.795</td>\n",
              "      <td>16.0</td>\n",
              "      <td>240.208</td>\n",
              "      <td>7.21</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.169</td>\n",
              "      <td>0.70</td>\n",
              "      <td>75.27</td>\n",
              "      <td>0.634</td>\n",
              "      <td>10432858.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111838</th>\n",
              "      <td>FRO</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Faeroe Islands</td>\n",
              "      <td>2024-01-04</td>\n",
              "      <td>34658.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>652484.139</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>527.138</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.308</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.67</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53117.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300719</th>\n",
              "      <td>STP</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>2020-08-26</td>\n",
              "      <td>892.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3922.724</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.398</td>\n",
              "      <td>65.965</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>212.841</td>\n",
              "      <td>18.7</td>\n",
              "      <td>2.886</td>\n",
              "      <td>2.162</td>\n",
              "      <td>3052.714</td>\n",
              "      <td>32.3</td>\n",
              "      <td>270.113</td>\n",
              "      <td>2.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.340</td>\n",
              "      <td>2.90</td>\n",
              "      <td>70.39</td>\n",
              "      <td>0.625</td>\n",
              "      <td>227393.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d86c8896-9ed1-4aa1-b113-797c7caf5fe9')\"\n",
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              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-d86c8896-9ed1-4aa1-b113-797c7caf5fe9 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-d86c8896-9ed1-4aa1-b113-797c7caf5fe9');\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",
              "\n",
              "\n",
              "<div id=\"df-bf1edb71-80f4-4b93-98c3-ac143c20df75\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf1edb71-80f4-4b93-98c3-ac143c20df75')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bf1edb71-80f4-4b93-98c3-ac143c20df75 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CAMPOS COM VALORES NULOS\n",
        "(df.isnull().sum() / df.shape[0]).sort_values(ascending=False).apply(lambda x: f'{round(x, 2):.2f}%' if x % 1 != 0 else f'{int(x)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPmGSSDm4AgO",
        "outputId": "e64fb68f-f95f-48d9-8559-fa951df67eeb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekly_icu_admissions_per_million             0.97%\n",
              "weekly_icu_admissions                         0.97%\n",
              "excess_mortality_cumulative_per_million       0.97%\n",
              "excess_mortality                              0.97%\n",
              "excess_mortality_cumulative                   0.97%\n",
              "excess_mortality_cumulative_absolute          0.97%\n",
              "weekly_hosp_admissions_per_million            0.94%\n",
              "weekly_hosp_admissions                        0.94%\n",
              "icu_patients                                  0.90%\n",
              "icu_patients_per_million                      0.90%\n",
              "hosp_patients_per_million                     0.90%\n",
              "hosp_patients                                 0.90%\n",
              "total_boosters_per_hundred                    0.87%\n",
              "total_boosters                                0.87%\n",
              "new_vaccinations                              0.82%\n",
              "new_tests                                     0.81%\n",
              "new_tests_per_thousand                        0.81%\n",
              "people_fully_vaccinated                       0.80%\n",
              "people_fully_vaccinated_per_hundred           0.80%\n",
              "people_vaccinated                             0.80%\n",
              "people_vaccinated_per_hundred                 0.80%\n",
              "total_tests                                   0.79%\n",
              "total_tests_per_thousand                      0.79%\n",
              "total_vaccinations                            0.78%\n",
              "total_vaccinations_per_hundred                0.78%\n",
              "tests_per_case                                0.76%\n",
              "positive_rate                                 0.75%\n",
              "new_tests_smoothed_per_thousand               0.73%\n",
              "new_tests_smoothed                            0.73%\n",
              "tests_units                                   0.72%\n",
              "handwashing_facilities                        0.62%\n",
              "reproduction_rate                             0.52%\n",
              "new_people_vaccinated_smoothed_per_hundred    0.52%\n",
              "new_people_vaccinated_smoothed                0.52%\n",
              "new_vaccinations_smoothed_per_million         0.51%\n",
              "new_vaccinations_smoothed                     0.51%\n",
              "extreme_poverty                               0.50%\n",
              "stringency_index                              0.49%\n",
              "male_smokers                                  0.43%\n",
              "female_smokers                                0.42%\n",
              "hospital_beds_per_thousand                    0.31%\n",
              "human_development_index                       0.25%\n",
              "aged_65_older                                 0.24%\n",
              "gdp_per_capita                                0.23%\n",
              "cardiovasc_death_rate                         0.22%\n",
              "aged_70_older                                 0.22%\n",
              "median_age                                    0.21%\n",
              "diabetes_prevalence                           0.18%\n",
              "total_deaths                                  0.16%\n",
              "total_deaths_per_million                      0.16%\n",
              "population_density                            0.15%\n",
              "total_cases_per_million                       0.10%\n",
              "total_cases                                   0.10%\n",
              "life_expectancy                               0.08%\n",
              "continent                                     0.05%\n",
              "new_cases_smoothed                            0.03%\n",
              "new_cases_smoothed_per_million                0.03%\n",
              "new_deaths_smoothed_per_million               0.03%\n",
              "new_deaths_smoothed                           0.03%\n",
              "new_cases                                     0.03%\n",
              "new_cases_per_million                         0.03%\n",
              "new_deaths                                    0.03%\n",
              "new_deaths_per_million                        0.03%\n",
              "population                                       0%\n",
              "date                                             0%\n",
              "location                                         0%\n",
              "iso_code                                         0%\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CAMPOS COM VALORES AUSENTES\n",
        "((df.isna().sum() / df.shape[0]) * 100).sort_values(ascending=False).apply(lambda x: f'{round(x, 2):.2f}%' if x % 1 != 0 else f'{int(x)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pH3ke3Z77dSP",
        "outputId": "fa87e69d-c1eb-443f-bf83-d3dc82fa81ff"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekly_icu_admissions_per_million             97.24%\n",
              "weekly_icu_admissions                         97.24%\n",
              "excess_mortality_cumulative_per_million       96.60%\n",
              "excess_mortality                              96.60%\n",
              "excess_mortality_cumulative                   96.60%\n",
              "excess_mortality_cumulative_absolute          96.60%\n",
              "weekly_hosp_admissions_per_million            93.76%\n",
              "weekly_hosp_admissions                        93.76%\n",
              "icu_patients                                  90.02%\n",
              "icu_patients_per_million                      90.02%\n",
              "hosp_patients_per_million                     89.63%\n",
              "hosp_patients                                 89.63%\n",
              "total_boosters_per_hundred                    86.71%\n",
              "total_boosters                                86.71%\n",
              "new_vaccinations                              82.17%\n",
              "new_tests                                     80.53%\n",
              "new_tests_per_thousand                        80.53%\n",
              "people_fully_vaccinated                       80.36%\n",
              "people_fully_vaccinated_per_hundred           80.36%\n",
              "people_vaccinated                             79.55%\n",
              "people_vaccinated_per_hundred                 79.55%\n",
              "total_tests                                   79.50%\n",
              "total_tests_per_thousand                      79.50%\n",
              "total_vaccinations                            78.49%\n",
              "total_vaccinations_per_hundred                78.49%\n",
              "tests_per_case                                75.63%\n",
              "positive_rate                                 75.23%\n",
              "new_tests_smoothed_per_thousand               73.15%\n",
              "new_tests_smoothed                            73.15%\n",
              "tests_units                                   72.42%\n",
              "handwashing_facilities                        61.96%\n",
              "reproduction_rate                             52.27%\n",
              "new_people_vaccinated_smoothed_per_hundred    51.61%\n",
              "new_people_vaccinated_smoothed                51.61%\n",
              "new_vaccinations_smoothed_per_million         50.93%\n",
              "new_vaccinations_smoothed                     50.93%\n",
              "extreme_poverty                               50.05%\n",
              "stringency_index                              49.05%\n",
              "male_smokers                                  42.51%\n",
              "female_smokers                                41.72%\n",
              "hospital_beds_per_thousand                    31.42%\n",
              "human_development_index                       24.69%\n",
              "aged_65_older                                 23.69%\n",
              "gdp_per_capita                                22.50%\n",
              "cardiovasc_death_rate                         22.35%\n",
              "aged_70_older                                 21.76%\n",
              "median_age                                    20.97%\n",
              "diabetes_prevalence                           18.35%\n",
              "total_deaths                                  15.78%\n",
              "total_deaths_per_million                      15.78%\n",
              "population_density                            14.98%\n",
              "total_cases_per_million                       10.04%\n",
              "total_cases                                   10.04%\n",
              "life_expectancy                                7.89%\n",
              "continent                                      4.78%\n",
              "new_cases_smoothed                             3.14%\n",
              "new_cases_smoothed_per_million                 3.14%\n",
              "new_deaths_smoothed_per_million                3.06%\n",
              "new_deaths_smoothed                            3.06%\n",
              "new_cases                                      2.82%\n",
              "new_cases_per_million                          2.82%\n",
              "new_deaths                                     2.74%\n",
              "new_deaths_per_million                         2.74%\n",
              "population                                        0%\n",
              "date                                              0%\n",
              "location                                          0%\n",
              "iso_code                                          0%\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "A alta porcentagem de valores ausentes em determinadas variáveis pode ser atribuída a uma variedade de fatores como:\n",
        "\n",
        "1. **Natureza Volátil dos Dados Emergenciais:** Durante uma crise como a pandemia de COVID-19, a coleta e o registro de dados podem ser desafiadores devido à rápida evolução da situação. Isso pode levar a lacunas na documentação de casos, internações em UTI e mortalidade excessiva.\n",
        "\n",
        "2. **Disparidades na Capacidade de Coleta de Dados:** A infraestrutura de saúde varia significativamente entre os países, e aqueles com recursos limitados podem enfrentar dificuldades na coleta e no relato de dados de saúde precisos.\n",
        "\n",
        "3. **Priorização de Recursos:** Durante uma crise de saúde pública, os recursos dos sistemas de saúde podem ser direcionados principalmente para a prestação de assistência médica imediata, em detrimento da coleta de dados. Isso pode resultar em lacunas na documentação de internações hospitalares e na atualização de estatísticas relacionadas.\n",
        "\n",
        "4. **Desafios Logísticos:** Restrições logísticas, como acesso limitado a áreas afetadas, interrupções nos sistemas de transporte e falta de pessoal qualificado, podem prejudicar a coleta e o relato oportuno de dados.\n",
        "\n",
        "5. **Mudanças nas Prioridades de Coleta de Dados:** Durante a pandemia, as autoridades de saúde podem ter revisado suas prioridades de coleta de dados, concentrando-se em métricas mais imediatas, como novos casos e mortes, em detrimento de variáveis menos urgentes, como hospitalizações e mortalidade excessiva."
      ],
      "metadata": {
        "id": "nppTsgDZciVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TRANSFORMANDO TIPOS\n",
        "\n",
        "# TRANSFORMANDO O CAMPO PARA FORMATO DE DATA\n",
        "df.date = pd.to_datetime(df.date)\n",
        "\n",
        "# PREENCHENDO VALORES AUSENTES COM ZERO SÓ PARA FAZER A CONTAGEM ABAIXO\n",
        "df[['new_cases', 'total_deaths','new_deaths','total_cases']] = df[['new_cases', 'total_deaths','new_deaths','total_cases']].fillna(0).astype(int)"
      ],
      "metadata": {
        "id": "vqNmrz8n-DE8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Qual a data mais recente do dataset ?"
      ],
      "metadata": {
        "id": "GjQyJD-4b0LP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DATA DE ATUALIZAÇÃO MAIS RECENTE DO DATASET (COMO OS DADOS SÃO CONSOLIDADOS A MELHOR MANEIRA PARA PEGAR OS DADOS É ASSIM)\n",
        "DATA_MAX = df[df['new_cases'] > 0][\"date\"].max()\n",
        "\n",
        "print(DATA_MAX.strftime('%Y-%m-%d'))\n",
        "\n",
        "#2024-03-17"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YBMUYLr_4DL",
        "outputId": "24545568-f195-4019-a2f6-a449db9f1d1d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-03-17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Nas análises a seguir será utilizado o dataset com a visão consolidada do dia 17/03/2024**"
      ],
      "metadata": {
        "id": "DOS39xklQILt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATASET - VISÃO DO DIA 17/03/2024"
      ],
      "metadata": {
        "id": "hfh6MLDP9_nT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrando os códigos ISO menor ou igual a 3 para ver apenas os países\n",
        "df_eda = df.loc[(df.date == \"2024-03-17\") & (df['iso_code'].str.len() <= 3)]"
      ],
      "metadata": {
        "id": "zDlyE18z9-4R"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quais os cinco países com maior número de casos registrados por COVID-19?"
      ],
      "metadata": {
        "id": "hZNT-cB7Wyaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Os 5 países com maior número de casos acumulados\n",
        "df_eda.sort_values(by=\"total_cases\", ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "uqESGtDiKsbF",
        "outputId": "51f729f0-d13d-40d7-f93e-0667812fe602"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       iso_code      continent       location       date  total_cases  \\\n",
              "366043      USA  North America  United States 2024-03-17    103436829   \n",
              "69081       CHN           Asia          China 2024-03-17     99343195   \n",
              "156400      IND           Asia          India 2024-03-17     45032655   \n",
              "118047      FRA         Europe         France 2024-03-17     38997490   \n",
              "127251      DEU         Europe        Germany 2024-03-17     38437756   \n",
              "\n",
              "        new_cases  new_cases_smoothed  total_deaths  new_deaths  \\\n",
              "366043          0                 NaN       1182424         758   \n",
              "69081        3426             489.429        122046           0   \n",
              "156400       1241             177.286        533521          11   \n",
              "118047          0                 NaN        167985           0   \n",
              "127251          0                 NaN        174979           0   \n",
              "\n",
              "        new_deaths_smoothed  total_cases_per_million  new_cases_per_million  \\\n",
              "366043              108.286               305763.910                    NaN   \n",
              "69081                 0.000                69671.138                  2.403   \n",
              "156400                1.571                31776.397                  0.876   \n",
              "118047                  NaN               603427.621                    NaN   \n",
              "127251                  NaN               461051.095                    NaN   \n",
              "\n",
              "        new_cases_smoothed_per_million  total_deaths_per_million  \\\n",
              "366043                             NaN                  3495.298   \n",
              "69081                            0.343                    85.593   \n",
              "156400                           0.125                   376.468   \n",
              "118047                             NaN                  2599.316   \n",
              "127251                             NaN                  2098.829   \n",
              "\n",
              "        new_deaths_per_million  new_deaths_smoothed_per_million  \\\n",
              "366043                   2.241                            0.320   \n",
              "69081                    0.000                            0.000   \n",
              "156400                   0.008                            0.001   \n",
              "118047                     NaN                              NaN   \n",
              "127251                     NaN                              NaN   \n",
              "\n",
              "        reproduction_rate  icu_patients  icu_patients_per_million  \\\n",
              "366043                NaN        1259.0                     3.722   \n",
              "69081                 NaN           NaN                       NaN   \n",
              "156400                NaN           NaN                       NaN   \n",
              "118047                NaN           NaN                       NaN   \n",
              "127251                NaN           NaN                       NaN   \n",
              "\n",
              "        hosp_patients  hosp_patients_per_million  weekly_icu_admissions  \\\n",
              "366043         9530.0                     28.171                    NaN   \n",
              "69081             NaN                        NaN                    NaN   \n",
              "156400            NaN                        NaN                    NaN   \n",
              "118047            NaN                        NaN                    NaN   \n",
              "127251            NaN                        NaN                    NaN   \n",
              "\n",
              "        weekly_icu_admissions_per_million  weekly_hosp_admissions  \\\n",
              "366043                                NaN                 10810.0   \n",
              "69081                                 NaN                     NaN   \n",
              "156400                                NaN                     NaN   \n",
              "118047                                NaN                     NaN   \n",
              "127251                                NaN                     NaN   \n",
              "\n",
              "        weekly_hosp_admissions_per_million  total_tests  new_tests  \\\n",
              "366043                              31.955          NaN        NaN   \n",
              "69081                                  NaN          NaN        NaN   \n",
              "156400                                 NaN          NaN        NaN   \n",
              "118047                                 NaN          NaN        NaN   \n",
              "127251                                 NaN          NaN        NaN   \n",
              "\n",
              "        total_tests_per_thousand  new_tests_per_thousand  new_tests_smoothed  \\\n",
              "366043                       NaN                     NaN                 NaN   \n",
              "69081                        NaN                     NaN                 NaN   \n",
              "156400                       NaN                     NaN                 NaN   \n",
              "118047                       NaN                     NaN                 NaN   \n",
              "127251                       NaN                     NaN                 NaN   \n",
              "\n",
              "        new_tests_smoothed_per_thousand  positive_rate  tests_per_case  \\\n",
              "366043                              NaN            NaN             NaN   \n",
              "69081                               NaN            NaN             NaN   \n",
              "156400                              NaN            NaN             NaN   \n",
              "118047                              NaN            NaN             NaN   \n",
              "127251                              NaN            NaN             NaN   \n",
              "\n",
              "       tests_units  total_vaccinations  people_vaccinated  \\\n",
              "366043         NaN                 NaN                NaN   \n",
              "69081          NaN                 NaN                NaN   \n",
              "156400         NaN        2.206802e+09       1.027432e+09   \n",
              "118047         NaN                 NaN                NaN   \n",
              "127251         NaN                 NaN                NaN   \n",
              "\n",
              "        people_fully_vaccinated  total_boosters  new_vaccinations  \\\n",
              "366043                      NaN             NaN               NaN   \n",
              "69081                       NaN             NaN               NaN   \n",
              "156400              951988781.0     227380951.0            5719.0   \n",
              "118047                      NaN             NaN               NaN   \n",
              "127251                      NaN             NaN               NaN   \n",
              "\n",
              "        new_vaccinations_smoothed  total_vaccinations_per_hundred  \\\n",
              "366043                        NaN                             NaN   \n",
              "69081                         NaN                             NaN   \n",
              "156400                     2851.0                          155.72   \n",
              "118047                        NaN                             NaN   \n",
              "127251                        NaN                             NaN   \n",
              "\n",
              "        people_vaccinated_per_hundred  people_fully_vaccinated_per_hundred  \\\n",
              "366043                            NaN                                  NaN   \n",
              "69081                             NaN                                  NaN   \n",
              "156400                           72.5                                67.18   \n",
              "118047                            NaN                                  NaN   \n",
              "127251                            NaN                                  NaN   \n",
              "\n",
              "        total_boosters_per_hundred  new_vaccinations_smoothed_per_million  \\\n",
              "366043                         NaN                                    NaN   \n",
              "69081                          NaN                                    NaN   \n",
              "156400                       16.04                                    2.0   \n",
              "118047                         NaN                                    NaN   \n",
              "127251                         NaN                                    NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed  \\\n",
              "366043                             NaN   \n",
              "69081                              NaN   \n",
              "156400                           400.0   \n",
              "118047                             NaN   \n",
              "127251                             NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed_per_hundred  stringency_index  \\\n",
              "366043                                         NaN               NaN   \n",
              "69081                                          NaN               NaN   \n",
              "156400                                         0.0               NaN   \n",
              "118047                                         NaN               NaN   \n",
              "127251                                         NaN               NaN   \n",
              "\n",
              "        population_density  median_age  aged_65_older  aged_70_older  \\\n",
              "366043              35.608        38.3         15.413          9.732   \n",
              "69081              147.674        38.7         10.641          5.929   \n",
              "156400             450.419        28.2          5.989          3.414   \n",
              "118047             122.578        42.0         19.718         13.079   \n",
              "127251             237.016        46.6         21.453         15.957   \n",
              "\n",
              "        gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "366043       54225.446              1.2                151.089   \n",
              "69081        15308.712              0.7                261.899   \n",
              "156400        6426.674             21.2                282.280   \n",
              "118047       38605.671              NaN                 86.060   \n",
              "127251       45229.245              NaN                156.139   \n",
              "\n",
              "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "366043                10.79            19.1          24.6   \n",
              "69081                  9.74             1.9          48.4   \n",
              "156400                10.39             1.9          20.6   \n",
              "118047                 4.77            30.1          35.6   \n",
              "127251                 8.31            28.2          33.1   \n",
              "\n",
              "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "366043                     NaN                        2.77            78.86   \n",
              "69081                      NaN                        4.34            76.91   \n",
              "156400                   59.55                        0.53            69.66   \n",
              "118047                     NaN                        5.98            82.66   \n",
              "127251                     NaN                        8.00            81.33   \n",
              "\n",
              "        human_development_index    population  \\\n",
              "366043                    0.926  3.382899e+08   \n",
              "69081                     0.761  1.425887e+09   \n",
              "156400                    0.645  1.417173e+09   \n",
              "118047                    0.901  6.781300e+07   \n",
              "127251                    0.947  8.336984e+07   \n",
              "\n",
              "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
              "366043                                   NaN                          NaN   \n",
              "69081                                    NaN                          NaN   \n",
              "156400                                   NaN                          NaN   \n",
              "118047                                   NaN                          NaN   \n",
              "127251                                   NaN                          NaN   \n",
              "\n",
              "        excess_mortality  excess_mortality_cumulative_per_million  \n",
              "366043               NaN                                      NaN  \n",
              "69081                NaN                                      NaN  \n",
              "156400               NaN                                      NaN  \n",
              "118047               NaN                                      NaN  \n",
              "127251               NaN                                      NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d666cf7e-0bd1-4c77-9325-fd79d852b7d9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>total_boosters</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>total_boosters_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>new_people_vaccinated_smoothed</th>\n",
              "      <th>new_people_vaccinated_smoothed_per_hundred</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "      <th>population</th>\n",
              "      <th>excess_mortality_cumulative_absolute</th>\n",
              "      <th>excess_mortality_cumulative</th>\n",
              "      <th>excess_mortality</th>\n",
              "      <th>excess_mortality_cumulative_per_million</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>366043</th>\n",
              "      <td>USA</td>\n",
              "      <td>North America</td>\n",
              "      <td>United States</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>103436829</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1182424</td>\n",
              "      <td>758</td>\n",
              "      <td>108.286</td>\n",
              "      <td>305763.910</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3495.298</td>\n",
              "      <td>2.241</td>\n",
              "      <td>0.320</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1259.0</td>\n",
              "      <td>3.722</td>\n",
              "      <td>9530.0</td>\n",
              "      <td>28.171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10810.0</td>\n",
              "      <td>31.955</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.608</td>\n",
              "      <td>38.3</td>\n",
              "      <td>15.413</td>\n",
              "      <td>9.732</td>\n",
              "      <td>54225.446</td>\n",
              "      <td>1.2</td>\n",
              "      <td>151.089</td>\n",
              "      <td>10.79</td>\n",
              "      <td>19.1</td>\n",
              "      <td>24.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.77</td>\n",
              "      <td>78.86</td>\n",
              "      <td>0.926</td>\n",
              "      <td>3.382899e+08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69081</th>\n",
              "      <td>CHN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>China</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>99343195</td>\n",
              "      <td>3426</td>\n",
              "      <td>489.429</td>\n",
              "      <td>122046</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>69671.138</td>\n",
              "      <td>2.403</td>\n",
              "      <td>0.343</td>\n",
              "      <td>85.593</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147.674</td>\n",
              "      <td>38.7</td>\n",
              "      <td>10.641</td>\n",
              "      <td>5.929</td>\n",
              "      <td>15308.712</td>\n",
              "      <td>0.7</td>\n",
              "      <td>261.899</td>\n",
              "      <td>9.74</td>\n",
              "      <td>1.9</td>\n",
              "      <td>48.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.34</td>\n",
              "      <td>76.91</td>\n",
              "      <td>0.761</td>\n",
              "      <td>1.425887e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156400</th>\n",
              "      <td>IND</td>\n",
              "      <td>Asia</td>\n",
              "      <td>India</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>45032655</td>\n",
              "      <td>1241</td>\n",
              "      <td>177.286</td>\n",
              "      <td>533521</td>\n",
              "      <td>11</td>\n",
              "      <td>1.571</td>\n",
              "      <td>31776.397</td>\n",
              "      <td>0.876</td>\n",
              "      <td>0.125</td>\n",
              "      <td>376.468</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.206802e+09</td>\n",
              "      <td>1.027432e+09</td>\n",
              "      <td>951988781.0</td>\n",
              "      <td>227380951.0</td>\n",
              "      <td>5719.0</td>\n",
              "      <td>2851.0</td>\n",
              "      <td>155.72</td>\n",
              "      <td>72.5</td>\n",
              "      <td>67.18</td>\n",
              "      <td>16.04</td>\n",
              "      <td>2.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>450.419</td>\n",
              "      <td>28.2</td>\n",
              "      <td>5.989</td>\n",
              "      <td>3.414</td>\n",
              "      <td>6426.674</td>\n",
              "      <td>21.2</td>\n",
              "      <td>282.280</td>\n",
              "      <td>10.39</td>\n",
              "      <td>1.9</td>\n",
              "      <td>20.6</td>\n",
              "      <td>59.55</td>\n",
              "      <td>0.53</td>\n",
              "      <td>69.66</td>\n",
              "      <td>0.645</td>\n",
              "      <td>1.417173e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118047</th>\n",
              "      <td>FRA</td>\n",
              "      <td>Europe</td>\n",
              "      <td>France</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>38997490</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>167985</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>603427.621</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2599.316</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>122.578</td>\n",
              "      <td>42.0</td>\n",
              "      <td>19.718</td>\n",
              "      <td>13.079</td>\n",
              "      <td>38605.671</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86.060</td>\n",
              "      <td>4.77</td>\n",
              "      <td>30.1</td>\n",
              "      <td>35.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.98</td>\n",
              "      <td>82.66</td>\n",
              "      <td>0.901</td>\n",
              "      <td>6.781300e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127251</th>\n",
              "      <td>DEU</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Germany</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>38437756</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>174979</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>461051.095</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2098.829</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>237.016</td>\n",
              "      <td>46.6</td>\n",
              "      <td>21.453</td>\n",
              "      <td>15.957</td>\n",
              "      <td>45229.245</td>\n",
              "      <td>NaN</td>\n",
              "      <td>156.139</td>\n",
              "      <td>8.31</td>\n",
              "      <td>28.2</td>\n",
              "      <td>33.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "      <td>81.33</td>\n",
              "      <td>0.947</td>\n",
              "      <td>8.336984e+07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nota-se que Estados Unidos, China e Índia são líderes em número de casos acumulados (***Dataset consolidado de 17/03/2024***)"
      ],
      "metadata": {
        "id": "wuhjl8I_d1D5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quais os cinco países com maior número de mortes acumuladas por COVID-19?"
      ],
      "metadata": {
        "id": "ljSC9_FHXUrd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Os 5 países com maior número de mortes acumuladas\n",
        "df_eda.sort_values(by=\"total_deaths\", ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "0UYV4yBdd0K6",
        "outputId": "7a57be49-2894-4c41-b0d7-d180067f5f4b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       iso_code      continent       location       date  total_cases  \\\n",
              "366043      USA  North America  United States 2024-03-17    103436829   \n",
              "47583       BRA  South America         Brazil 2024-03-17     37519960   \n",
              "156400      IND           Asia          India 2024-03-17     45032655   \n",
              "285144      RUS         Europe         Russia 2024-03-17     24031776   \n",
              "218624      MEX  North America         Mexico 2024-03-17      7702809   \n",
              "\n",
              "        new_cases  new_cases_smoothed  total_deaths  new_deaths  \\\n",
              "366043          0                 NaN       1182424         758   \n",
              "47583           0               0.000        702116           0   \n",
              "156400       1241             177.286        533521          11   \n",
              "285144      24056            3436.571        402374          77   \n",
              "218624          0               0.000        334958           0   \n",
              "\n",
              "        new_deaths_smoothed  total_cases_per_million  new_cases_per_million  \\\n",
              "366043              108.286               305763.910                    NaN   \n",
              "47583                 0.000               174257.347                  0.000   \n",
              "156400                1.571                31776.397                  0.876   \n",
              "285144               11.000               166064.723                166.232   \n",
              "218624                0.000                60412.236                  0.000   \n",
              "\n",
              "        new_cases_smoothed_per_million  total_deaths_per_million  \\\n",
              "366043                             NaN                  3495.298   \n",
              "47583                            0.000                  3260.901   \n",
              "156400                           0.125                   376.468   \n",
              "285144                          23.747                  2780.491   \n",
              "218624                           0.000                  2627.037   \n",
              "\n",
              "        new_deaths_per_million  new_deaths_smoothed_per_million  \\\n",
              "366043                   2.241                            0.320   \n",
              "47583                    0.000                            0.000   \n",
              "156400                   0.008                            0.001   \n",
              "285144                   0.532                            0.076   \n",
              "218624                   0.000                            0.000   \n",
              "\n",
              "        reproduction_rate  icu_patients  icu_patients_per_million  \\\n",
              "366043                NaN        1259.0                     3.722   \n",
              "47583                 NaN           NaN                       NaN   \n",
              "156400                NaN           NaN                       NaN   \n",
              "285144                NaN           NaN                       NaN   \n",
              "218624                NaN           NaN                       NaN   \n",
              "\n",
              "        hosp_patients  hosp_patients_per_million  weekly_icu_admissions  \\\n",
              "366043         9530.0                     28.171                    NaN   \n",
              "47583             NaN                        NaN                    NaN   \n",
              "156400            NaN                        NaN                    NaN   \n",
              "285144            NaN                        NaN                    NaN   \n",
              "218624            NaN                        NaN                    NaN   \n",
              "\n",
              "        weekly_icu_admissions_per_million  weekly_hosp_admissions  \\\n",
              "366043                                NaN                 10810.0   \n",
              "47583                                 NaN                     NaN   \n",
              "156400                                NaN                     NaN   \n",
              "285144                                NaN                     NaN   \n",
              "218624                                NaN                     NaN   \n",
              "\n",
              "        weekly_hosp_admissions_per_million  total_tests  new_tests  \\\n",
              "366043                              31.955          NaN        NaN   \n",
              "47583                                  NaN          NaN        NaN   \n",
              "156400                                 NaN          NaN        NaN   \n",
              "285144                                 NaN          NaN        NaN   \n",
              "218624                                 NaN          NaN        NaN   \n",
              "\n",
              "        total_tests_per_thousand  new_tests_per_thousand  new_tests_smoothed  \\\n",
              "366043                       NaN                     NaN                 NaN   \n",
              "47583                        NaN                     NaN                 NaN   \n",
              "156400                       NaN                     NaN                 NaN   \n",
              "285144                       NaN                     NaN                 NaN   \n",
              "218624                       NaN                     NaN                 NaN   \n",
              "\n",
              "        new_tests_smoothed_per_thousand  positive_rate  tests_per_case  \\\n",
              "366043                              NaN            NaN             NaN   \n",
              "47583                               NaN            NaN             NaN   \n",
              "156400                              NaN            NaN             NaN   \n",
              "285144                              NaN            NaN             NaN   \n",
              "218624                              NaN            NaN             NaN   \n",
              "\n",
              "       tests_units  total_vaccinations  people_vaccinated  \\\n",
              "366043         NaN                 NaN                NaN   \n",
              "47583          NaN                 NaN                NaN   \n",
              "156400         NaN        2.206802e+09       1.027432e+09   \n",
              "285144         NaN                 NaN                NaN   \n",
              "218624         NaN                 NaN                NaN   \n",
              "\n",
              "        people_fully_vaccinated  total_boosters  new_vaccinations  \\\n",
              "366043                      NaN             NaN               NaN   \n",
              "47583                       NaN             NaN               NaN   \n",
              "156400              951988781.0     227380951.0            5719.0   \n",
              "285144                      NaN             NaN               NaN   \n",
              "218624                      NaN             NaN               NaN   \n",
              "\n",
              "        new_vaccinations_smoothed  total_vaccinations_per_hundred  \\\n",
              "366043                        NaN                             NaN   \n",
              "47583                         NaN                             NaN   \n",
              "156400                     2851.0                          155.72   \n",
              "285144                        NaN                             NaN   \n",
              "218624                        NaN                             NaN   \n",
              "\n",
              "        people_vaccinated_per_hundred  people_fully_vaccinated_per_hundred  \\\n",
              "366043                            NaN                                  NaN   \n",
              "47583                             NaN                                  NaN   \n",
              "156400                           72.5                                67.18   \n",
              "285144                            NaN                                  NaN   \n",
              "218624                            NaN                                  NaN   \n",
              "\n",
              "        total_boosters_per_hundred  new_vaccinations_smoothed_per_million  \\\n",
              "366043                         NaN                                    NaN   \n",
              "47583                          NaN                                    NaN   \n",
              "156400                       16.04                                    2.0   \n",
              "285144                         NaN                                    NaN   \n",
              "218624                         NaN                                    NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed  \\\n",
              "366043                             NaN   \n",
              "47583                              NaN   \n",
              "156400                           400.0   \n",
              "285144                             NaN   \n",
              "218624                             NaN   \n",
              "\n",
              "        new_people_vaccinated_smoothed_per_hundred  stringency_index  \\\n",
              "366043                                         NaN               NaN   \n",
              "47583                                          NaN               NaN   \n",
              "156400                                         0.0               NaN   \n",
              "285144                                         NaN               NaN   \n",
              "218624                                         NaN               NaN   \n",
              "\n",
              "        population_density  median_age  aged_65_older  aged_70_older  \\\n",
              "366043              35.608        38.3         15.413          9.732   \n",
              "47583               25.040        33.5          8.552          5.060   \n",
              "156400             450.419        28.2          5.989          3.414   \n",
              "285144               8.823        39.6         14.178          9.393   \n",
              "218624              66.444        29.3          6.857          4.321   \n",
              "\n",
              "        gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "366043       54225.446              1.2                151.089   \n",
              "47583        14103.452              3.4                177.961   \n",
              "156400        6426.674             21.2                282.280   \n",
              "285144       24765.954              0.1                431.297   \n",
              "218624       17336.469              2.5                152.783   \n",
              "\n",
              "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "366043                10.79            19.1          24.6   \n",
              "47583                  8.11            10.1          17.9   \n",
              "156400                10.39             1.9          20.6   \n",
              "285144                 6.18            23.4          58.3   \n",
              "218624                13.06             6.9          21.4   \n",
              "\n",
              "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "366043                     NaN                        2.77            78.86   \n",
              "47583                      NaN                        2.20            75.88   \n",
              "156400                  59.550                        0.53            69.66   \n",
              "285144                     NaN                        8.05            72.58   \n",
              "218624                  87.847                        1.38            75.05   \n",
              "\n",
              "        human_development_index    population  \\\n",
              "366043                    0.926  3.382899e+08   \n",
              "47583                     0.765  2.153135e+08   \n",
              "156400                    0.645  1.417173e+09   \n",
              "285144                    0.824  1.447133e+08   \n",
              "218624                    0.779  1.275041e+08   \n",
              "\n",
              "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
              "366043                                   NaN                          NaN   \n",
              "47583                                    NaN                          NaN   \n",
              "156400                                   NaN                          NaN   \n",
              "285144                                   NaN                          NaN   \n",
              "218624                                   NaN                          NaN   \n",
              "\n",
              "        excess_mortality  excess_mortality_cumulative_per_million  \n",
              "366043               NaN                                      NaN  \n",
              "47583                NaN                                      NaN  \n",
              "156400               NaN                                      NaN  \n",
              "285144               NaN                                      NaN  \n",
              "218624               NaN                                      NaN  "
            ],
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              "      <th>excess_mortality_cumulative</th>\n",
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              "        document.querySelector('#df-7c4f5899-b6bb-426b-b6d6-39981711eb0f 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-7c4f5899-b6bb-426b-b6d6-39981711eb0f');\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",
              "\n",
              "\n",
              "<div id=\"df-742436a8-0350-411a-aae3-37ebc0cf206e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-742436a8-0350-411a-aae3-37ebc0cf206e')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-742436a8-0350-411a-aae3-37ebc0cf206e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 Países com maiores totais de mortes acumuladas\n",
        " (***Dataset consolidado de 17/03/2024***)"
      ],
      "metadata": {
        "id": "AYbUCPH2XilM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráficos: TOP 5 Países"
      ],
      "metadata": {
        "id": "4EIhFpQ0ep0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Por Total Acumulado\n",
        "TOP5_TotalCasesCountry = df_eda.loc[:, ['location', 'total_cases']].sort_values(by=\"total_cases\", ascending=False)[:5]\n",
        "TOP5_TotalDeathsCountry = df_eda.loc[:, ['location', 'total_deaths']].sort_values(by=\"total_deaths\", ascending=False)[:5]\n",
        "\n",
        "# Por Milhão de Habitantes\n",
        "TOP5_TotalCasesCountryPerMillion = df_eda.loc[:, ['location', 'total_cases_per_million']].sort_values(by=\"total_cases_per_million\", ascending=False)[:5]\n",
        "TOP5_TotalDeathsCountryPerMillion = df_eda.loc[:, ['location', 'total_deaths_per_million']].sort_values(by=\"total_deaths_per_million\", ascending=False)[:5]\n",
        "\n",
        "# Paleta de cores viridis\n",
        "cor = plt.get_cmap('viridis')\n",
        "\n",
        "fig, ((ax_cases, ax_deaths), (ax_cases_per_million, ax_deaths_per_million)) = plt.subplots(2, 2, figsize=(13, 11))\n",
        "\n",
        "# Gráfico 1: TOP 5 PAÍSES COM MAIOR NÚMERO DE CASOS DE COVID\n",
        "ax_cases.bar('location', 'total_cases', data=TOP5_TotalCasesCountry, color=cor(0.2))\n",
        "ax_cases.set_title('Top 5 Países com Maior Número de Casos de COVID')\n",
        "ax_cases.tick_params(axis='x', rotation=45)  # Rotacionar os rótulos do eixo x\n",
        "\n",
        "# Gráfico 2: TOP 5 PAÍSES COM MAIOR NÚMERO DE MORTES POR COVID\n",
        "ax_deaths.bar('location', 'total_deaths', data=TOP5_TotalDeathsCountry, color=cor(0.4))\n",
        "ax_deaths.set_title('Top 5 Países com Maior Número de Mortes por COVID')\n",
        "ax_deaths.tick_params(axis='x', rotation=45)  # Rotacionar os rótulos do eixo x\n",
        "\n",
        "# Gráfico 3: TOP 5 PAÍSES COM MAIOR NÚMERO DE CASOS POR MILHÃO\n",
        "ax_cases_per_million.bar('location', 'total_cases_per_million', data=TOP5_TotalCasesCountryPerMillion, color=cor(0.6))\n",
        "ax_cases_per_million.set_title('Top 5 Países com Maior Número de Casos Por Milhão de Habitantes')\n",
        "ax_cases_per_million.set_ylabel('Total de Casos Por Milhão')\n",
        "ax_cases_per_million.tick_params(axis='x', rotation=45)  # Rotacionar os rótulos do eixo x\n",
        "\n",
        "# Gráfico 4: TOP 5 PAÍSES COM MAIOR NÚMERO DE MORTES POR MILHÃO\n",
        "ax_deaths_per_million.bar('location', 'total_deaths_per_million', data=TOP5_TotalDeathsCountryPerMillion, color=cor(0.8))\n",
        "ax_deaths_per_million.set_title('Top 5 Países com Maior Número de Mortes Por Milhão de Habitantes')\n",
        "ax_deaths_per_million.tick_params(axis='x', rotation=45)  # Rotacionar os rótulos do eixo x\n",
        "\n",
        "# Definir rótulos do eixo y para exibir números com pontos de milhar\n",
        "ax_cases.yaxis.set_major_formatter(formatter)\n",
        "ax_deaths.yaxis.set_major_formatter(formatter)\n",
        "ax_cases_per_million.yaxis.set_major_formatter(formatter)\n",
        "ax_deaths_per_million.yaxis.set_major_formatter(formatter)\n",
        "\n",
        "# Ajustar o layout\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "id": "xLze7--3Lygu",
        "outputId": "3adaf770-36de-4dac-b345-45c43c57e00b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1100 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: Evolução dos Casos Confirmados e de Óbitos"
      ],
      "metadata": {
        "id": "hk4STqFIg5ub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Definindo o tamanho da figura\n",
        "plt.figure(figsize=(20, 8))\n",
        "\n",
        "df_word = df.loc[df.location == \"World\"]\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "df_world_cases = df_word[[\"date\", \"total_cases\"]]\n",
        "sns.lineplot(x='date', y='total_cases', data=df_world_cases)\n",
        "plt.xticks(rotation=45)\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Total de Casos Confirmados')\n",
        "plt.title('Evolução dos Casos Confirmados de COVID-19 no Mundo')\n",
        "#plt.gca().yaxis.set_major_formatter(formatter)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "df_world_deaths = df_word[[\"date\", \"total_deaths\"]]\n",
        "sns.lineplot(x='date', y='total_deaths', data=df_world_deaths)\n",
        "plt.xticks(rotation=45)\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Total de Óbitos')\n",
        "plt.title('Evolução dos Óbitos Totais de COVID-19 no Mundo')\n",
        "\n",
        "#plt.gca().yaxis.set_major_formatter(formatter)\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "cellView": "form",
        "id": "5CJu3idCT1BX",
        "outputId": "e6ae29be-6bbd-40a9-ca89-c9dc8e8884bd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ],
            "image/png": 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YJ97jK8Y9EB8fL5/PF/4+AIFAQPHx8XrsscfUvXt3LVu2TPn5+ZJOP4F7xRVXaO3atcrOzvZy2RHtXJoEAgFJ0qhRo9SmTRvP1hzpGjRooHr16qm4uFiS5JxTfHy8Bg8erAEDBmjr1q1atGiRpNP7JDs7W2vWrOGJqWpyLj3c/z5NeMstt+jnP/+5V0uOCpwntnCW2MQ+sYV9Yg97BNGA69wWzgKbmMFtYQa3h7PEFs4Sm9gntrBPbGKfeM/nHO+HUNMqKirUtWtXpaSkaNGiRYqJiVEgEFBsbKycc8rMzFRWVpbmzp0r55x8Pp/XS45459IENaOqqkp33HGH9u3bpwULFqhly5Zn/fi1116ruLg4vfXWWx6tMLrQwybOE1s4S2xin9jCPrGHPYJowHVuC2eBTcx8ttDDHs4SWzhLbGKf2MI+sYl94j2+YryGhUIhJSQkaPbs2frwww913333SVL4ovf5fLrxxhtVVlYmSVz0NeBcm6D6OecUFxenF198UTt27NCoUaNUVlZ21vc1ueGGG3TgwAGdOnXKw5VGB3rYxHliC2eJTewTW9gn9rBHEA24zm3hLLCJmc8WetjDWWILZ4lN7BNb2Cc2sU9s4MZ4DfP7/QoGg7r00ks1d+5cLVy4UIMGDdJXX30Vfs3OnTvVuHFjBYNBD1caPWhij8/nU2VlpS644AKtWLFCa9eu1YABA1RQUBBusHHjRjVt2lR+P5/Gqhs9bOJzly30sIkuttDDHpogGnCd20IPm5j5bKGHPXzusoUeNtHFFnrYRBcbeCv1GnbmLRGOHz+uiooKbdy4Uf3791fLli3VpEkTNW3aVIsXL9bHH3+sjIwMr5cbFWhiTzAYVExMjA4ePKjKykqdPHlS1113nRo0aKBAIKDWrVtr5cqVWr16tTp06OD1ciMePWzic5ct9LDh228xRRdv0cOeUCh01j+g0wTRgOvcFnrYxMxnCz3s4XOXLfSwgXnPFnrYxAxuE48VVpNvP2/gnAtf9Lt27VLbtm2Vn5+vnj17asuWLbr++uvVokULXXDBBVq3bh0XfTWgSe1wZgDctWuXOnTooJUrV6p169bKz8/X6NGj1atXL+Xm5io/P58BsAbQwyY+d9lCD++deYr2zFnPGe8tethz4MABSf95Ol063YkmiCTMe7bQo/Zg5rOFHvbwucsWeniPec8WetjEDG4bXzFeDT777DPNnz9fu3fvVteuXdW1a1elp6dLknbv3q3s7GzdfPPNmjlzpkKhkGJiYsJP9Hz7CRKcHzSx56uvvlJ5ebnatm37nR/74osvlJGRob59+2rGjBlyztGgmtHDpp07d+of//iHtm3bpuuuu05ZWVlq1qyZJKm0tFTZ2dm66aab+NxVQ+hh07Zt2zR9+nTt3r1bmZmZGjhwoNLS0iTRxQv0sGfbtm3KycnR7bffrpdfflnSf/7RnSaIFMx7ttDDJmY+W+hhD/OeLfSwiXnPFnrYxAxuH3/C51lJSYm6dOmikpISff7555o1a5Z69eql9957T5K0aNEiDRw4UDNnzpTP51NMTMxZP/+bb3eB84Mm9mzdulWdO3fWY489pi1btnznxwsKCjR8+HDNmDFDPp+Pw6Ca0cOm4uJide3aVUuWLNFbb72lBx54QK+++qqCwaCqqqq0ZMkSPnfVIHrYVFxcrLy8PB0+fFihUEjLly/XwoUL5ZxTVVWVFi9erAEDBtClhtDDppKSEtWtW1fFxcUaMWKEJCkmJkaVlZXhz11nzniaoDZi3rOFHjYx89lCD3uY92yhh03Me7bQwy5m8FrA4bwJBAJuwIAB7s477wx/rLCw0N11110uJibGvfPOO+HXoWbQxJ49e/a4vLw8l5mZ6Tp37uyGDx/uiouLz3pNZWWlR6uLPvSwadeuXe6SSy5x48aNC//5P/LII65Nmzbu5MmTzjnnjhw54uUSowo9bNqxY4dr2bKle/TRR8MfGz58uBs1atRZr+OMrxn0sOvtt992bdu2dVOmTHEZGRluxIgR4R8rLS31cGXA/x/zni30sImZzxZ62MO8Zws9bGLes4UetjGD28cjh+dRKBRSaWmpUlNTwx/r2LGjJk2apLvvvls33XSTPvnkk+88BYLqQxN7Pv30UyUmJmru3LkaOXKkCgsL9dxzz2nz5s3h18TFxXm4wuhCD3uCwaAWL16srKwsPfDAA+GvDhg9erQqKyu1bds2SVJSUpKXy4wa9LApGAzq3XffVc+ePfXQQw+Fv5dW3bp1tXnzZnXv3l2DBg3SmjVrwm9JhepDD9syMjLUqVMn3XXXXRo6dKg+/vhjjRkzRsOHD9eyZctUVVXl9RKBn4x5zxZ62MTMZws9bGHes4UeNjHv2UIP+5jB7ePG+HkUFxenSy+9VP/85z91+PDh8MebN2+ucePG6frrr9eECRN09OhRD1cZXWhiT15enh5//HFlZmZq8ODBuv/++8ODYHFxcfh1Zw7tUCjk1VKjAj3siYmJUVJSkq644gqlpKSE/+HQ5/Pp6NGjOnTo0Hd+Dn/JrT70sCkmJkbXXHONLU8NtAAAEWZJREFUxowZo8aNG8vn8+lPf/qTZs2apauvvlpXXXWVKisrNXDgQO3cuZO3oqpm9LCtSZMm2rJli0pLSzVixAjdf//9+stf/qLZs2crLy9PcXFxCgaDXi8T+EmY92yhh03MfLbQwxbmPVvoYRPzni30sI8Z3D5ujJ9nV155pU6dOqXZs2fr2LFj4Y+npqbqhhtu0MaNG1VeXu7hCqMPTWypU6eOLrvssvD/Hzp0qEaNGqXCwkJNnTo1/JT0hAkTtGnTJr6XVjWjh02DBw/WqFGjJP1nyGvYsKFSUlJUr1698OuWLFmi0tJS/pJbzehhU1pamtq3by9Jqqio0Nq1a/XGG2/o97//vf74xz/q/vvv1/Hjx7V9+3aPVxod6GFTVVWVEhISlJKSouPHj6tevXpauXKlqqqq1KZNG82aNUuS+OpN1GrMe7bQwx5mPlvoYQ/zni30sIl5zxZ62MUMXjvEer2A2mzv3r3asGGDKisrdfHFFysnJ0e33XabVq1apZkzZ6pu3brq16+fmjRpIknKzc1VvXr1zhoOcX7RxJ5vNmnZsqU6deokn88n55ycc/L7/Ro8eLAkadq0aZo6daqOHj2qN954Q7feeqvHq4889LDp//rcJZ1+e6Qzf1Hy+/3y+/3hoW/cuHGaPXu21q5d69m6IxU9bPpvn7+CwaASEhK0dOlS+f1+hUIh+f1+NWnSRMnJyeEzH+cXPez5ZpNWrVopOzs7/FasnTp10vbt2/Xyyy/rww8/1NKlS1VcXKwpU6YoNjZWzzzzjMerB34c5j1b6GETM58t9LCHec8WetjEvGcLPWxiBq/FauZbmUeeTZs2udatW7vOnTu7Zs2auZycHLdw4cLwjw8ZMsRlZGS40aNHu+3bt7v9+/e7sWPHurZt27oDBw54uPLIRRN7/q8mr7/++lmvCQaD4f9+5ZVXXFxcnEtKSnKFhYU1vNrIRw+bfkwX55w7fPiwa968ufvoo4/chAkTXJ06dVx+fr4HK45s9LDpx3QJhUJn/f9HHnnE5ebmuv3799fkUqMCPez5oSZPPPGE8/l8Li0tza1fv945d/rz2Isvvuh27Njh1bKBc8K8Zws9bGLms4Ue9jDv2UIPm5j3bKGHTczgtRs3xn+C7du3u4suusiNHTvWHTlyxBUUFLjBgwe7YcOGuVOnToVf98c//tF169bN+Xw+16lTJ5eSkuI2bNjg4cojF03s+b4mgUDgrAM7FAq5QCDgRo0a5Ro3buw2b97s4cojEz1sOpcux44dc1lZWe6qq65yderUcQUFBR6uPDLRw6Zz6eKcc//+97/dww8/7Bo3buyKioo8WnXkooc939ekqqrKOedcVVWVGzlypFu3bp1z7j//cPLNf4wHLGPes4UeNjHz2UIPe5j3bKGHTcx7ttDDJmbw2o8b4+eooqLCjRkzxt12222uoqIi/PFXXnnFNW3a9DtPPh84cMAtX77crV692pWWltb0cqMCTew51ybOObdu3Trn8/l44rMa0MOmc+1y5MgR17JlS9ekSRO3cePGml5uxKOHTefaJT8/340cOdJlZmbSpRrQw56fcsYDtQ3zni30sImZzxZ62MO8Zws9bGLes4UeNjGDRwa+x/g5CoVCuuiii9SuXTvFx8fLOSefz6e8vDw1aNBAVVVV4df5/X41bdpUvXv39njVkY0m9vzYJt+Um5urQ4cOqVGjRjW/4AhHD5vOtUtSUpLuvvtu/epXv1J6erpHq45c9LDpXLvk5OTo5MmT+sMf/qALL7zQo1VHLnrY81PO+DN/JwZqC+Y9W+hhEzOfLfSwh3nPFnrYxLxnCz1sYgaPDNwYP0d16tTRzTffrLS0tLM+3qhRI8XFxYUvfL/fr8LCQmVlZXmxzKhCE3t+bBNJZzVJSkqq0XVGC3rYdC5dCgoKlJOTo0cffbSmlxk16GHTuXRZv369OnXqpG7dutX0MqMGPez5KWc8AzlqG+Y9W+hhEzOfLfSwh3nPFnrYxLxnCz1sYgaPDBT5Efbt26d169ZpxYoVCoVC4Ys+GAzK5/NJksrLy3X48OHwzxk/frx69uypgwcPyjnnybojGU3sOR9NzrwO/3/0sOmndrnmmmv43FUN6GHTT+3Sq1cvulQDetjD34MRDbjObaGHTcx8ttDDHuY9W+hhE/OeLfSwib8LR6Dqfaf22q+oqMi1bNnStW3b1iUlJbn09HS3YMECd/DgQeecc6FQyDnn3GeffeaaN2/uDh065CZMmODq1q3rCgoKvFx6xKKJPTSxhR420cUWethEF1voYQ9NEA24zm2hh010sYUe9tDEFnrYRBdb6GETXSITN8a/R1lZmUtPT3fjxo1zO3bscHv27HH9+vVz7dq1c48//rgrKysLv/arr75yWVlZrl+/fi4+Pp6LvprQxB6a2EIPm+hiCz1soos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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A análise dos gráficos revela uma tendência interessante nos casos confirmados de COVID-19. Apesar da persistência de um número considerável de casos, a partir de janeiro de 2023, observa-se uma estabilização. Por outro lado, o gráfico de óbitos acumulados indica um declínio, provavelmente associado às campanhas de vacinação em massa que teve no mundo todo."
      ],
      "metadata": {
        "id": "V6FJyX5u6Vug"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: Progresso de Vacinações & Doses aplicadas"
      ],
      "metadata": {
        "id": "dxzFs0BPjTL6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Definir os dados de vacinação\n",
        "df_vac = df_word[['date', 'total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated']]\n",
        "\n",
        "# Agrupar os dados por data e calcular o total de doses administradas, pessoas vacinadas e pessoas totalmente vacinadas para cada data\n",
        "df_vac_daily = df_vac.groupby('date').agg({\n",
        "    'total_vaccinations': 'max',  # Total de doses administradas\n",
        "    'people_vaccinated': 'max',   # Total de pessoas vacinadas\n",
        "    'people_fully_vaccinated': 'max'  # Total de pessoas totalmente vacinadas\n",
        "}).reset_index()\n",
        "\n",
        "# Estilo do gráfico\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Gráfico de doses administradas\n",
        "sns.lineplot(x='date', y='total_vaccinations', data=df_vac_daily, label='Total de Doses Administradas', color='blue')\n",
        "\n",
        "# Gráfico de pessoas vacinadas\n",
        "sns.lineplot(x='date', y='people_vaccinated', data=df_vac_daily, label='Pessoas Vacinadas', color='green')\n",
        "\n",
        "# Gráfico de pessoas totalmente vacinadas\n",
        "sns.lineplot(x='date', y='people_fully_vaccinated', data=df_vac_daily, label='Pessoas Totalmente Vacinadas', color='orange')\n",
        "\n",
        "# Rótulos\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Número de Pessoas / Doses')\n",
        "plt.title('Progresso da Vacinação')\n",
        "\n",
        "# Rotacionar rótulos do eixo x\n",
        "plt.xticks(rotation=45)\n",
        "#plt.gca().yaxis.set_major_formatter(formatter)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "cellView": "form",
        "id": "uuB0PKT5lmpE",
        "outputId": "75030fdf-56c5-49a9-9d9a-3d9ee008fa0a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No gráfico mostra o total de doses aplicadas no mundo todo, assim como o número de pessoas vacinadas com pelo menos uma dose (se for o caso) e a pessoas totalmente vacinadas."
      ],
      "metadata": {
        "id": "pHafAhuZ73Gx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: PIB per Capita X Morte por Milhão"
      ],
      "metadata": {
        "id": "_X9FRbYXQliy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Removendo as entradas que possuem valores ausentes nas colunas 'gdp_per_capita' e 'total_deaths'\n",
        "df_scatter = df.loc[df.date == DATA_MAX].dropna(subset=['gdp_per_capita', 'total_deaths_per_million'])\n",
        "\n",
        "colunas_renomear = {\n",
        "    \"continent\": \"Continente\",\n",
        "    \"location\": \"País\",\n",
        "    \"gdp_per_capita\": \"PIB per Capita\",\n",
        "    \"total_deaths_per_million\":\"Óbitos por Milhão de Hab.\"\n",
        "\n",
        "}\n",
        "\n",
        "df_scatter = df_scatter.rename(columns=colunas_renomear)\n",
        "\n",
        "# Atualizar os campos de Continente\n",
        "df_scatter['Continente'] = df_scatter['Continente'].replace({'North America': 'America', 'South America': 'America'})\n",
        "\n",
        "fig = px.scatter(df_scatter, x=\"PIB per Capita\", y=\"Óbitos por Milhão de Hab.\", color=\"Continente\",\n",
        "                 hover_name='País',\n",
        "                 hover_data={'Continente': True, 'PIB per Capita': ':.2f', \"Óbitos por Milhão de Hab.\": ':.2f'},\n",
        "                 title='Gráfico Interativo de PIB per Capita X Óbitos por Milhão')\n",
        "\n",
        "fig.update_traces(marker=dict(size=8))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "cellView": "form",
        "id": "V2NFsMn0hoC1",
        "outputId": "f38e50b5-ca6a-48a2-b30a-bb652ffc60b6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Com esse gráfico, é possível obter uma visão mais clara da distribuição dos casos de óbito em relação ao Produto Interno Bruto per capita (PIB per Capita). Essa representação visual revela insights valiosos sobre a interseção entre os níveis de mortalidade e a prosperidade econômica de diferentes países."
      ],
      "metadata": {
        "id": "1RIi0ZCXS0Op"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SViA5VZ7DYLA"
      },
      "source": [
        "## Análise Exploratória para o Brasil\n",
        "\n",
        "<img alt=\"Colaboratory logo\" width=\"80%\" src=\"https://images.unsplash.com/photo-1527374071603-76de89807336?q=80&w=2076&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D\">\n",
        "\n",
        "Nesta fase, destacaremos a análise centrada no Brasil e nos propomos a responder os seguintes questionamentos:\n",
        "\n",
        "* Gráfico do total de casos e mortes acumulados\n",
        "* Gráfico de dispersão do percentual de vacinados em relação ao percentual de óbitos.\n",
        "* Gráfico: Progresso de Vacinações & Doses aplicadas\n",
        "* Indicadores Brasil"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDQA502_hAwI"
      },
      "source": [
        "# copia dataframe\n",
        "df_br = df.loc[df.location == \"Brazil\"].copy()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: Casos Totais e Mortes Totais"
      ],
      "metadata": {
        "id": "We66qXa8ZBh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Plotar gráficos de linha em escalas linear e logarítmica para os casos totais e mortes totais.\n",
        "x = list(range(df_br.shape[0]))\n",
        "z = df_br['total_cases']\n",
        "z2 = df_br['total_deaths']\n",
        "\n",
        "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(9, 7))\n",
        "ax1.set_title('Casos no Brasil')\n",
        "ax1.set_xlabel('Dias')\n",
        "ax1.set_ylabel('Casos')\n",
        "ax1.plot(x, z)\n",
        "\n",
        "ax2.set_title('Casos no Brasil (Escala logarítmica)')\n",
        "ax2.set_xlabel('Dias')\n",
        "ax2.set_ylabel('Casos')\n",
        "ax2.set_yscale('log')\n",
        "ax2.plot(x, z)\n",
        "\n",
        "ax3.set_title('Mortes no Brasil')\n",
        "ax3.set_xlabel('Dias')\n",
        "ax3.set_ylabel('Mortes')\n",
        "ax3.plot(x, z2)\n",
        "\n",
        "ax4.set_title('Mortes no Brasil (Escala logarítmica')\n",
        "ax4.set_xlabel('Dias')\n",
        "ax4.set_ylabel('Mortes')\n",
        "ax4.set_yscale('log')\n",
        "ax4.plot(x, z2)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "cellView": "form",
        "id": "eXgbGYCbqeTv",
        "outputId": "877fb277-e0cf-490d-9687-80d2e78c698d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: Dispersão do percentual de vacinados em relação ao percentual de óbitos."
      ],
      "metadata": {
        "id": "L1MZa5PamzRU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Calcular as proporções\n",
        "df_br['percentual_mortes'] = df_br['total_deaths'] / df_br['total_cases'].replace(0, 1)\n",
        "df_br['percentual_vacinados'] = df_br['people_vaccinated'] / df_br['population'].replace(0, 1)\n",
        "\n",
        "# Filtrar os dados a partir da primeira data de vacinação no Brasil\n",
        "df_filtrado = df_br.loc[df_br['date'] >= '2021-01-17']\n",
        "\n",
        "# Definir os limites das faixas de percentual de vacinação\n",
        "faixas_vacinacao = [(0, 0.25), (0.26, 0.50), (0.51, 0.75), (0.76, 1)]\n",
        "\n",
        "# Criar categorias com base nos limites definidos\n",
        "df_filtrado['faixa_vacinacao'] = pd.cut(df_filtrado['percentual_vacinados'], bins=[faixa[0] for faixa in faixas_vacinacao] + [1], labels=[\"0-25%\", \"26-50%\", \"51-75%\", \"76-100%\"])\n",
        "\n",
        "# Configurar o tamanho da figura\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 8)\n",
        "\n",
        "# Criar o gráfico de dispersão\n",
        "ax = sns.scatterplot(data=df_filtrado, x=\"percentual_vacinados\", y=\"percentual_mortes\", hue=\"faixa_vacinacao\", palette=\"magma\", s=100)\n",
        "ax.set(xlabel='Percentual de vacinados', ylabel='Percentual de mortes')\n",
        "ax.set_title(\"Percentual de vacinados x Percentual de mortes\")\n",
        "ax.grid(False)\n",
        "\n",
        "# Definir os limites e rótulos dos ticks no eixo x\n",
        "intervalos = np.arange(0, 1.1, 0.25)\n",
        "ax.set_xticks(intervalos)\n",
        "ax.set_xticklabels([\"0%\", \"25%\", \"50%\", \"75%\", \"100%\"])\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "cellView": "form",
        "id": "5iYVY8v6btbL",
        "outputId": "aa8917a3-e900-4014-d885-3622ef9e03c8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Percebe-se a queda brusca no percentual de mortes, quando o percentual de vacinados passa de 75%."
      ],
      "metadata": {
        "id": "YcKY1v1_mbC-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gráfico: Progresso de Vacinações & Doses aplicadas"
      ],
      "metadata": {
        "id": "grSguj87PfFL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Definir os dados de vacinação\n",
        "df_vac = df_br[['date', 'total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated']]\n",
        "\n",
        "# Agrupar os dados por data e calcular o total de doses administradas, pessoas vacinadas e pessoas totalmente vacinadas para cada data\n",
        "df_vac_daily = df_vac.groupby('date').agg({\n",
        "    'total_vaccinations': 'max',  # Total de doses administradas\n",
        "    'people_vaccinated': 'max',   # Total de pessoas vacinadas\n",
        "    'people_fully_vaccinated': 'max'  # Total de pessoas totalmente vacinadas\n",
        "}).reset_index()\n",
        "\n",
        "# Estilo do gráfico\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Gráfico de doses administradas\n",
        "sns.lineplot(x='date', y='total_vaccinations', data=df_vac_daily, label='Total de Doses Administradas', color='blue')\n",
        "\n",
        "# Gráfico de pessoas vacinadas\n",
        "sns.lineplot(x='date', y='people_vaccinated', data=df_vac_daily, label='Pessoas Vacinadas', color='green')\n",
        "\n",
        "# Gráfico de pessoas totalmente vacinadas\n",
        "sns.lineplot(x='date', y='people_fully_vaccinated', data=df_vac_daily, label='Pessoas Totalmente Vacinadas', color='orange')\n",
        "\n",
        "# Rótulos\n",
        "plt.xlabel('Data')\n",
        "plt.ylabel('Número de Pessoas / Doses')\n",
        "plt.title('Progresso da Vacinação')\n",
        "\n",
        "# Rotacionar rótulos do eixo x\n",
        "plt.xticks(rotation=45)\n",
        "plt.gca().yaxis.set_major_formatter(formatter)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "cellView": "form",
        "id": "FeHfLb3NOyxW",
        "outputId": "9799b06b-4f6c-43b7-9a96-63d5b3422e1a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Indicadores Brasil"
      ],
      "metadata": {
        "id": "8UzAQQaEbgjh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title\n",
        "# Obter a data máxima\n",
        "data_maxima = df_br['date'].max()\n",
        "\n",
        "# Selecionar a linha correspondente à data máxima e obter a população\n",
        "populacao_brasil = df_br.loc[df_br['date'] == data_maxima, 'population'].iloc[0]\n",
        "\n",
        "# Últimos dados de vacinação\n",
        "ultima_data = df_br['date'].max()\n",
        "dados_ultima_data = df_br[df_br['date'] == df_br[df_br['people_vaccinated'].notnull()]['date'].max()].iloc[0]\n",
        "\n",
        "# # Pessoas vacinadas com pelo menos uma dose\n",
        "pessoas_com_uma_dose = dados_ultima_data['people_vaccinated']\n",
        "\n",
        "# # Pessoas totalmente vacinadas\n",
        "pessoas_totalmente_vacinadas = dados_ultima_data['people_fully_vaccinated']\n",
        "\n",
        "dados_ultima_data[[\"people_vaccinated\",\"people_fully_vaccinated\"]]\n",
        "\n",
        "# # Calcular as porcentagens\n",
        "porcentagem_vacinacao_com_uma_dose = (pessoas_com_uma_dose / populacao_brasil) * 100\n",
        "porcentagem_vacinacao_totalmente_vacinadas = (pessoas_totalmente_vacinadas / populacao_brasil) * 100\n",
        "\n",
        "print(f\"População: {populacao_brasil:,.0f}\".replace(\",\", \".\"))\n",
        "print(f\"População com pelo menos uma dose : {pessoas_com_uma_dose:,.0f}\".replace(\",\", \".\"))\n",
        "print(f\"População totalmente vacinada: {pessoas_totalmente_vacinadas:,.0f}\\n\".replace(\",\", \".\"))\n",
        "\n",
        "print(f\"Porcentagem da população com pelo menos uma dose: {porcentagem_vacinacao_com_uma_dose:.2f}%\")\n",
        "print(f\"Porcentagem da população totalmente vacinada: {porcentagem_vacinacao_totalmente_vacinadas:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "QqmEJRhoSLUF",
        "outputId": "6810f893-bcf4-4955-c1e7-7d45b02da85e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "População: 215.313.504\n",
            "População com pelo menos uma dose : 189.643.431\n",
            "População totalmente vacinada: 176.164.186\n",
            "\n",
            "Porcentagem da população com pelo menos uma dose: 88.08%\n",
            "Porcentagem da população totalmente vacinada: 81.82%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCsiXvM3BcsO"
      },
      "source": [
        "## Conclusão\n",
        "\n",
        "Com o dataset analisado de 2024 sobre o covid 19, temos em destaques às seguintes informações\n",
        "\n",
        "* Estados Unidos liderou em números absolutos de casos e mortes por covid 19\n",
        "* Brasil ficou em segundo lugar no números absolutos de mortes registradas\n",
        "* Peru teve maior número de mortes registradas por milhão Habitantes\n",
        "* Graças às campanhas de vacinação pelo mundo a tendência por mortes de Covid 19 regrediu bastante, apesar do gráfico de tendência mostrar que ainda há muitos casos pelo mundo.\n",
        "* O percentual de mortes diminui conforme aumenta-se o percentual de pessoas vacinadas.\n",
        "* Mais de 85% da população do Brasil tomou ao menos uma dose da Vacina contra o Covid 19.\n",
        "* Quase 82% da população do Brasil foi totalmente vacinada.\n",
        "\n"
      ]
    }
  ]
}