Tarefa10KryzianSousa.ipynb

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      "source": [
        "##Nome: Kryzian Sousa dos Santos RA: 11202021159"
      ],
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        "**1)** Para este item você deverá usar o arquivo [com todos os resultados do campeonato Brasileiro de futebol de 2018 (tabelaBrasileirao2018.csv encontrado na pasta aula5/dados)](https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/tabelaBrasileirao2018.csv) (dados obtidos [desta](<https://pt.wikipedia.org/wiki/Resultados_do_Campeonato_Brasileiro_de_Futebol_de_2018_-_S%C3%A9rie_A_(primeiro_turno)>) e [desta](<https://pt.wikipedia.org/wiki/Resultados_do_Campeonato_Brasileiro_de_Futebol_de_2018_-_S%C3%A9rie_A_(segundo_turno)>) página da Wikipedia).\n",
        "\n",
        "- Calcule (e mostre o resultado) a porcentagem de jogos que o time da casa (mandante) ganhou o jogo.\n",
        "\n",
        "- Calcule (e mostre o resultado) a porcentagem de jogos que o time da casa (mandante) não perdeu o jogo."
      ]
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      "source": [
        "import pandas as pd #Comando para importar a biblioteca pandas\n",
        "import numpy as np #Comando para importar a biblioteca numpy\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/tabelaBrasileirao2018.csv') #Comando para leitura do arquivo csv\n",
        "df"
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              "           Data  Turno         Mandante  Placar do Mandante  \\\n",
              "0    14/04/2018      1         Cruzeiro                   0   \n",
              "1    14/04/2018      1          Vitória                   2   \n",
              "2    14/04/2018      1           Santos                   2   \n",
              "3    15/04/2018      1  América Mineiro                   3   \n",
              "4    15/04/2018      1    Internacional                   2   \n",
              "..          ...    ...              ...                 ...   \n",
              "375   2/12/2018      2       Fluminense                   1   \n",
              "376   2/12/2018      2            Ceará                   0   \n",
              "377   2/12/2018      2        Palmeiras                   3   \n",
              "378   2/12/2018      2           Grêmio                   1   \n",
              "379   2/12/2018      2            Sport                   2   \n",
              "\n",
              "     Placar do Visitante        Visitante  \\\n",
              "0                      1           Grêmio   \n",
              "1                      2         Flamengo   \n",
              "2                      0            Ceará   \n",
              "3                      0            Sport   \n",
              "4                      0            Bahia   \n",
              "..                   ...              ...   \n",
              "375                    0  América Mineiro   \n",
              "376                    0    Vasco da Gama   \n",
              "377                    2          Vitória   \n",
              "378                    0      Corinthians   \n",
              "379                    1           Santos   \n",
              "\n",
              "                                   Estádio Horário  \\\n",
              "0         Estádio Mineirão, Belo Horizonte   16:00   \n",
              "1               Estádio Barradão, Salvador   19:00   \n",
              "2           Estádio do Pacaembu, São Paulo   21:00   \n",
              "3    Estádio Independência, Belo Horizonte   11:00   \n",
              "4          Estádio Beira-Rio, Porto Alegre   16:00   \n",
              "..                                     ...     ...   \n",
              "375    Estádio do Maracanã, Rio de Janeiro   17:00   \n",
              "376            Estádio Castelão, Fortaleza   17:00   \n",
              "377              Allianz Parque, São Paulo   17:00   \n",
              "378          Arena do Grêmio, Porto Alegre   17:00   \n",
              "379         Estádio Ilha do Retiro, Recife   17:00   \n",
              "\n",
              "                                      Gols do Mandante  \\\n",
              "0                                                  NaN   \n",
              "1                          Yago 13' (pen) Denílson 76'   \n",
              "2                                  Pio 41' Rodrygo 49'   \n",
              "3                       Serginho 1', 40' Carlinhos 36'   \n",
              "4                                  Nico López 37', 60'   \n",
              "..                                                 ...   \n",
              "375                                        Richard 40'   \n",
              "376                                                NaN   \n",
              "377  Edu Dracena 42' Gustavo Scarpa 56' Bruno Henri...   \n",
              "378                                           Jael 11'   \n",
              "379                          Rogério 83' Hernane 90+1'   \n",
              "\n",
              "              Gols do Visitante  Público                              Árbitro  \\\n",
              "0                     54' André    15446            PR Rodolpho Toski Marques   \n",
              "1    1' Lucas Paquetá 72' Réver    12669                      MT Wagner Reway   \n",
              "2                           NaN    12268         SC Rodrigo D'Alonso Ferreira   \n",
              "3                           NaN     3230             DF Sávio Pereira Sampaio   \n",
              "4                           NaN    18460   GO Eduardo Tomaz de Aquino Valadão   \n",
              "..                          ...      ...                                  ...   \n",
              "375                         NaN    34870                      PR Rafael Traci   \n",
              "376                         NaN    57223                     SP Raphael Claus   \n",
              "377     70' (pen) Yago 72' Luan    41256               SC Héber Roberto Lopes   \n",
              "378                         NaN    38974           MG Ricardo Marques Ribeiro   \n",
              "379               90+3' Rodrygo    15214                  RS Anderson Daronco   \n",
              "\n",
              "     Renda (R$)  \n",
              "0      418305.0  \n",
              "1      238994.0  \n",
              "2      526550.0  \n",
              "3       36293.0  \n",
              "4      796275.0  \n",
              "..          ...  \n",
              "375    139932.5  \n",
              "376   1128494.0  \n",
              "377   3514618.3  \n",
              "378   1450578.0  \n",
              "379    225305.0  \n",
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              "      <td>14/04/2018</td>\n",
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              "      <td>Cruzeiro</td>\n",
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              "      <td>Estádio Barradão, Salvador</td>\n",
              "      <td>19:00</td>\n",
              "      <td>Yago 13' (pen) Denílson 76'</td>\n",
              "      <td>1' Lucas Paquetá 72' Réver</td>\n",
              "      <td>12669</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Ceará</td>\n",
              "      <td>Estádio do Pacaembu, São Paulo</td>\n",
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              "      <td>15/04/2018</td>\n",
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              "      <td>3</td>\n",
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              "      <td>Sport</td>\n",
              "      <td>Estádio Independência, Belo Horizonte</td>\n",
              "      <td>11:00</td>\n",
              "      <td>Serginho 1', 40' Carlinhos 36'</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3230</td>\n",
              "      <td>DF Sávio Pereira Sampaio</td>\n",
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              "      <th>4</th>\n",
              "      <td>15/04/2018</td>\n",
              "      <td>1</td>\n",
              "      <td>Internacional</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>Estádio Beira-Rio, Porto Alegre</td>\n",
              "      <td>16:00</td>\n",
              "      <td>Nico López 37', 60'</td>\n",
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              "      <td>2/12/2018</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Richard 40'</td>\n",
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              "      <td>34870</td>\n",
              "      <td>PR Rafael Traci</td>\n",
              "      <td>139932.5</td>\n",
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              "      <td>0</td>\n",
              "      <td>Vasco da Gama</td>\n",
              "      <td>Estádio Castelão, Fortaleza</td>\n",
              "      <td>17:00</td>\n",
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              "      <td>57223</td>\n",
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              "      <td>Vitória</td>\n",
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              "      <td>17:00</td>\n",
              "      <td>Edu Dracena 42' Gustavo Scarpa 56' Bruno Henri...</td>\n",
              "      <td>70' (pen) Yago 72' Luan</td>\n",
              "      <td>41256</td>\n",
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      "source": [
        "x=df[df[\"Placar do Mandante\"]>df[\"Placar do Visitante\"]].shape[0] #Comando para calcular a qtdd de vezes que o mandante venceu os jogos\n",
        "print(\"Esta é a quantidade de vezes que o mandante venceu os jogos\", x) #Comando para mostrar a quantidade de jogos em que o mandante venceu os jogos"
      ],
      "metadata": {
        "id": "4UYCdTjccwP9",
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        "outputId": "6c57ccdf-0fcd-4dbf-8a5e-793667c16298"
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      "execution_count": 36,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Esta é a quantidade de vezes que o mandante venceu os jogos 202\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Vitorias= (x/380)*100 #Comando para calcular a porcentagem em que o mandante venceu os jogos\n",
        "print(\"Esta é a porcentagem em que o mandante venceu os jogos\", Vitorias,) #Comando para mostrar a porcentagem em que o mandante venceu os jogos"
      ],
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      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Esta é a porcentagem em que o mandante venceu os jogos 53.1578947368421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df[df[\"Placar do Mandante\"]<df[\"Placar do Visitante\"]].shape[0] #Comando para calcular a qtdd de vezes que o mandante perdeu os jogos\n",
        "print(\"Esta é a quantidade de vezes que o mandante perdeu os jogos\", y) #Comando para mostrar a quantidade de jogos em que o mandante perdeu os jogos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvGzLJU-KBtx",
        "outputId": "94115c1e-a1ec-4fdc-9346-5aa996c6d1ea"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Esta é a quantidade de vezes que o mandante perdeu os jogos 68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Derrotas= (y/380)*100 #Comando para calcular a porcentagem em que o mandante perdeu os jogos\n",
        "print(\"Esta é a porcentagem em que o mandante venceu os jogos\", Derrotas,) #Comando para mostrar a porcentagem em que o mandante perdeu os jogos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OP2ZwbjP8Ka",
        "outputId": "40c57c2d-64e6-4143-b3a9-d69d5258cd94"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Esta é a porcentagem em que o mandante venceu os jogos 17.894736842105264\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e196ZVj2zndt"
      },
      "source": [
        "**2)**  Para este item você deverá usar o arquivo com a [taxa de inflação mensal (IGP-DI) no Brasil de fevereiro de 1944 a junho de 2019](https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/inflacaoMensal.csv) (dados disponibilizados por Ipeadata). \n",
        "\n",
        "- Faça um gráfico da taxa de inflação mensal em função do tempo.\n",
        "- Mostre em que mês e ano e qual foi a maior taxa de inflação mensal medida neste período (fevereiro de 1944 a junho de 2019).\n",
        "\n",
        "Dica: para fazer o gráfico, note que um mês corresponde a um doze avos de ano. Para facilitar a criação do gráfico você pode criar uma outra coluna que corresponda ao ano adicionado da fração correspondente ao mês."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #Comando para importar a biblioteca numpy\n",
        "import pandas as pd #Comando para importar a biblioteca pandas\n",
        "import matplotlib.pyplot as plt #Comando para importar matplotlib\n",
        "\n",
        "inflacao = pd.read_csv('https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/inflacaoMensal.csv') #Comando para leitura do arquivo csv\n",
        "\n",
        "inflacao['Ano + Mês'] = inflacao['Ano'] + inflacao['Mês']/12 #Comando para criação da coluna conforme dica do exercício\n",
        "\n",
        "meses = [\"Janeiro\", \"Fevereiro\", \"Março\", \"Abril\", \"Maio\", \"Junho\", \"Julho\", \"Agosto\", \"Setembro\", \"Outubro\", \"Novembro\",\"Dezembro\"] #Comando para criação de vetores com os meses\n",
        "\n",
        "imax = max(inflacao['Inflação']) #Comando para identificar o valor e a posição de maior inflação\n",
        "b = np.where(inflacao['Inflação'] == imax)\n",
        "a = b[0]\n",
        "\n",
        "print(\"A maior taxa de inflacao ocorreu em \" + #Comando para retornar maior inflação encontrada no comando anterior\n",
        "      str(meses[inflacao['Mês'].at[a[0]]-1]) + \" de \" +\n",
        "      str(inflacao['Ano'].at[a[0]]) + \".\\n\")\n",
        "\n",
        "plt.figure(figsize=(12,5)) #Comando para fazer o gráfico com medidas específicas\n",
        "plt.plot(inflacao['Ano + Mês'], inflacao['Inflação'], color='gray') #Comando para fazer gráfico de acordo com o enunciado\n",
        "plt.xlabel('Ano') #Nome da abscissa\n",
        "plt.ylabel('Taxa de Inflacao (%)') #Nome da ordenada\n",
        "plt.title('Taxa de inflacao ao longo dos anos') #Título do gráfico\n",
        "plt.plot(inflacao['Ano + Mês'].at[a[0]], inflacao['Inflação'].at[a[0]], #Colocar ponto onde foi a maior inflação\n",
        "         '.', color='r')\n",
        "plt.annotate((\"Maior taxa de inflacao\"), #Edição do comentário no plt.plot\n",
        "             (inflacao['Ano + Mês'].at[a[0]], inflacao['Inflação'].at[a[0]]),\n",
        "             fontsize=10, fontweight='bold')\n",
        "plt.xkcd() #Comando para deixar o gráfico no modo cartoon\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "-0j7F2asei1v",
        "outputId": "bd0ab861-4e1e-4529-9cba-8c539542c046"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A maior taxa de inflacao ocorreu em Março de 1990.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTM5tNpqzndu"
      },
      "source": [
        "**3)** Para este item você pode usar [esse histórico de visualização do Netflix](https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/netflix.csv) ou, se você tiver acesso ao Netflix, usar o seu [histórico](https://www.netflix.com/viewingactivity) (após entrar com a sua senha, role até o final da página e clique em \"Baixar tudo\").\n",
        "\n",
        "   + Mostre quais são os 10 programas mais vistos.\n",
        "   + Em qual mês do ano foi assistido mais programas? \n",
        "    \n",
        "A sequência de instruções acima cria uma coluna com o tipo do programa (Filme/Série), uma coluna com o nome do programa e uma outra coluna com o nome do episódio (no caso de ser uma série). Isso facilitará realizar a tarefa.\n",
        "\n",
        "Na sequência acima, *data* é o nome da variável na qual foi guardado o DataFrame com a informação vinda do arquivo de histórico. Se você der outro nome, mude o nome da variável no código acima. \n",
        "\n",
        "Pode ser que o seu arquivo esteja em português. Nesse caso, o campo 'Title' terá o nome 'Título' e você deverá alterar o código acima substituindo 'Title' por 'Título'."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #Comando para importar a biblioteca numpy\n",
        "import pandas as pd #Comando para importar a biblioteca pandas\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/netflix.csv') #Comando para leitura do arquivo csv\n",
        "df['Categoria']= 'Filme' #Comando para criação de colunas\n",
        "df['Categoria'][df['Title'].str.contains(\": Temporada|: Stranger|: Parte\")]= 'Série' #Comando para criação de colunas\n",
        "df['Programa'] = df['Title'] #Comando para criação de colunas\n",
        "df[['Programa','Episódio']] = df[df['Categoria']=='Série']['Title'].str.split(pat = ': Temporada|: Stranger Things|: Parte', expand = True, n = 1) #Comando para criação de colunas\n",
        "df.loc[df['Categoria']=='Filme','Programa'] = df.loc[df['Categoria']=='Filme','Title'] #Comando para criação de colunas\n",
        "df = df.drop(columns= 'Title') #Comando para criação de colunas\n",
        "print(\"Programas mais assistidos e quantidade vista: \\n\" + #Print para mostrar os programas mais vistos\n",
        "      str(df['Programa'].value_counts().head(10)))\n",
        "df.sort_values(['Date']) #Comando para mostrar em que mês e ano obteve a maior quantidade vista\n",
        "df['Mês e Ano'] = df['Date'].str.slice(3)\n",
        "mes_ano = df['Mês e Ano'].value_counts().head(1)\n",
        "print(\"\\nMês e ano com maior número de programas vistos e quantidade: \\n\" + #Print para mostrar qual mês e ano obteve a maior quantidade vista\n",
        "      str(mes_ano))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sX1GBKxbfyWD",
        "outputId": "564a8f3a-412c-43e0-e7b9-9507479040b8"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Programas mais assistidos e quantidade vista: \n",
            "House of Cards                           73\n",
            "Jornada nas Estrelas: A nova geração     64\n",
            "Star Trek: Enterprise                    63\n",
            "Jornada nas Estrelas: Deep Space Nine    46\n",
            "Star Trek: Discovery                     42\n",
            "Jornada nas estrelas                     34\n",
            "The Crown                                32\n",
            "Cobra Kai                                30\n",
            "Aggretsuko                               28\n",
            "Downton Abbey                            25\n",
            "Name: Programa, dtype: int64\n",
            "\n",
            "Mês e ano com maior número de programas vistos e quantidade: \n",
            "11/2016    34\n",
            "Name: Mês e Ano, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "KjHnxtbdzndv"
      },
      "source": [
        "**4)** Para este item você deverá usar o arquivo [com todos os resultados do campeonato Brasileiro de futebol de 2018 (tabelaBrasileirao2018.csv encontrado na pasta aula5/dados)](https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/tabelaBrasileirao2018.csv) (dados obtidos [desta](<https://pt.wikipedia.org/wiki/Resultados_do_Campeonato_Brasileiro_de_Futebol_de_2018_-_S%C3%A9rie_A_(primeiro_turno)>) e [desta](<https://pt.wikipedia.org/wiki/Resultados_do_Campeonato_Brasileiro_de_Futebol_de_2018_-_S%C3%A9rie_A_(segundo_turno)>) página da Wikipedia).\n",
        "\n",
        "- Calcule qual foi a média de Renda para cada Estádio.\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #Comando para importar a biblioteca numpy\n",
        "import pandas as pd #Comando para importar a biblioteca pandas\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/BMClab/BasesComputacionais/master/dados/tabelaBrasileirao2018.csv') #Comando para leitura do arquivo csv"
      ],
      "metadata": {
        "id": "SUrxSDEqf9Z2"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "renda_total=df.groupby('Estádio')['Renda (R$)'].sum() #Comando para juntar estádios e somar as rendas\n",
        "qtd_j_estadio=df.groupby('Estádio')['Estádio'].count() #Comando para unir estádios e contar a qtdd de jogos por estádio\n",
        "renda_media=renda_total/qtd_j_estadio #Comando para dividir a renda pela quantidade de estádios\n",
        "print(\"Renda média para cada estádio em R$: \\n\" + str(round(renda_media,2))) #Comando para mostrar resultados calculados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y82WyLKSJqcG",
        "outputId": "ee4a033b-3f81-401c-be17-f8ebbc919d9f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Renda média para cada estádio em R$: \n",
            "Estádio\n",
            "Allianz Parque, São Paulo                 2184977.30\n",
            "Arena Condá, Chapecó                       256148.16\n",
            "Arena Corinthians, São Paulo              1335614.75\n",
            "Arena Fonte Nova, Salvador                 391600.29\n",
            "Arena Pernambuco, São Lourenço da Mata     593910.00\n",
            "Arena da Baixada, Curitiba                 266011.05\n",
            "Arena do Grêmio, Porto Alegre              760596.89\n",
            "Estádio Barradão, Salvador                  96398.37\n",
            "Estádio Beira-Rio, Porto Alegre            850073.66\n",
            "Estádio Castelão, Fortaleza                623102.71\n",
            "Estádio Engenhão, Rio de Janeiro           167022.74\n",
            "Estádio Ilha do Retiro, Recife             182628.61\n",
            "Estádio Indepedência, Belo Horizonte       113144.50\n",
            "Estádio Independência, Belo Horizonte      185470.32\n",
            "Estádio Mineirão, Belo Horizonte           325315.47\n",
            "Estádio Nacional, Brasília                2614730.00\n",
            "Estádio Presidente Vargas, Fortaleza       103040.80\n",
            "Estádio São Januário, Rio de Janeiro       242278.44\n",
            "Estádio Vila Belmiro, Santos               164668.08\n",
            "Estádio Vila Capanema, Curitiba            153496.39\n",
            "Estádio de Pituaçu, Salvador               297475.00\n",
            "Estádio do Café, Londrina                 1953570.00\n",
            "Estádio do Maracanã, Rio de Janeiro        849158.13\n",
            "Estádio do Morumbi, São Paulo             1188518.74\n",
            "Estádio do Pacaembu, São Paulo             709281.14\n",
            "dtype: float64\n"
          ]
        }
      ]
    }
  ]
}