MegaSenaProjetoFinal.ipynb

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    "colab": {
      "name": "MegaSenaProjetoFinal.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wesleyclzns/notebook-bcc-ufabc-2021.3/blob/main/MegaSenaProjetoFinal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mW7jsiRiZtoj"
      },
      "source": [
        "Este notebook servirá como template para o Projeto Final de BCC\n",
        "\n",
        "A ideia do projeto final é você usar os conceitos e métodos aprendidos durante o curso para criar um programa/notebook que resolva um problema de seu interesse.\n",
        "\n",
        "O projeto final deverá ser entregue como o link para um notebook Colab.\n",
        "\n",
        "Para facilitar a compreensão, por favor, usem uma combinação de texto e código.\n",
        "\n",
        "Este primeiro trecho de texto pode ser removido de seu trabalho.\n",
        "\n",
        "O trabalho poderá ser feito em grupos de **no máximo 3 pessoas**\n",
        "\n",
        "A primeira célula deverá ser, necessariamente, uma versão modificada da célula de texto abaixo\n",
        "\n",
        "**Regras para submissão trabalho**\n",
        "\n",
        "O trabalho deverá ser submetido via Moodle até a data de 7 de Dezembro (via atividade específica).\n",
        "\n",
        "Na atividade um dos membros do grupo deverá submeter o trabalho que deverá incluir, obrigatoriamente:\n",
        "1. Um link para um notebook colab\n",
        "2. Um link para uma gravação do google meet onde o trabalho é apresentado.\n",
        "\n",
        "ATENÇÃO-> VERIFIQUEM QUE OS LINKS ESTEJAM COM OS ACESSOS CORRETOS\n",
        "\n",
        "Na apresentação, todos os integrantes deverão estar com a webcam aberta e os membros do grupo devem apresentar, brevemente, o objetivo e desenvolvimento do trabalho. \n",
        "\n",
        "No início do vídeo, cada aluno deverá se apresentar. \n",
        "\n",
        "O vídeo deverá conter entre 5-10 minutos. \n",
        "\n",
        "Confirmar se a base de dados está no formato de download:\n",
        "https://sites.google.com/site/gdocs2direct/home\n",
        "\n",
        "Citem no notebook a origem da base de dados (caso você esteja usando uma).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNnxIeQZbGvc"
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      "source": [
        "Projeto Final de BCC\n",
        "\n",
        "**Grupo:**\n",
        "\n",
        "Flavia Aparecida de Morais Clapis; 11202112021; flavia.clapis@aluno.ufabc.edu.br; TNA10BIS0005-15SB\n",
        "\n",
        "Rodrigo Danilo R. De Carvalho; 21052613; rodrigo.danilo@aluno.ufabc.edu.br; TNA10BIS0005-15SB\n",
        "\n",
        "Matheus Santin; RA; EMAIL; TNA10BIS0005-15SB\n",
        "\n",
        "Wesley Café Calazans; 11202131858; ew.cafe@aluno.ufabc.edu.br; TNA10BIS0005-15SB\n",
        "\n",
        "**Objetivo do Projeto:**\n",
        "\n",
        "Neste item vocês devem descrever, brevemente, o objetivo do projeto de vocês. Pode ser uma descrição entre 5 a 10 linhas\n",
        "\n",
        "**Conceitos aprendidos e utilizados no projeto:**\n",
        "\n",
        "*É importante que vocês indiquem quais conceitos aprendidos serão utilizados no projeto de vocês. Da lista abaixo, destaquem (sublinhando ou deixando em negrito) os conceitos que vocês usarão. O projeto precisa utilizar, obrigatoriamente, pelo menos quatros dos conceitos abaixo* \n",
        "\n",
        "\n",
        "*   Manipulação de Base de Dados\n",
        "*   Representação Gráfico de Funções\n",
        "*   Estatística Descritiva\n",
        "*   Correlação e Regressão\n",
        "*   Programação Sequencial\n",
        "*   Programação Condicional\n",
        "*   Estruturas de Repetição\n",
        "\n",
        "\n",
        "Caso vocês optem por usar uma base de dados disponível, por favor já incluam o link nesta descrição.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2LOpmREdxG7"
      },
      "source": [
        "Por favor, na célula acima, deixem apenas as informações pedidas. O projeto de vocês deve começar a partir desta célula. A partir daqui, vocês podem misturar e combinar células de texto e código. \n",
        "\n",
        "Sempre expliquem o que cada célula está fazendo, seja por meio de texto ou por meio de comentários no código. Por exemplo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVRX2h2GeKWF"
      },
      "source": [
        "#Nesta primeira célula, iremos importar as bibliotecas necessárias para nosso projeto\n",
        "\n",
        "# import numpy as np\n",
        "# import pandas as pd\n",
        "\n"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvqAAkQDs7Xa"
      },
      "source": [
        "# Informações que temos sobre a MegaSena🤑\n",
        "\n",
        "Site da Mega Sena - http://loterias.caixa.gov.br/wps/portal/loterias/landing/megasena/\n",
        "\n",
        "### Probabilidade de Ganhar um jogo\n",
        "\n",
        "![Sem 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)\n",
        "\n",
        "\n",
        "###Quantidade de premios a receber acertando\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "###Como jogar\n",
        "A Mega-Sena paga milhões para o acertador dos 6 números sorteados. Ainda é possível ganhar prêmios ao acertar 4 ou 5 números dentre os 60 disponíveis no volante de apostas. Para realizar o sonho de ser o próximo milionário, você deve marcar de 6 a 15 números do volante, podendo deixar que o sistema escolha os números para você (Surpresinha) e/ou concorrer com a mesma aposta por 2, 4 ou 8 concursos consecutivos (Teimosinha).\n",
        "\n",
        "###Sorteios\n",
        "Os sorteios da Mega-Sena são realizados duas vezes por semana, às quartas e aos sábados. Para adequar o número do concurso da Mega da Virada, que deve ter final 0 ou 5 foram criadas as Mega-Semanas que são exclusividade da Mega-Sena. Os sorteios ocorrem em datas predeterminadas ao longo do ano. Na ocasião são realizados três concursos semanais, às terças, quintas e sábados. Confira no cronograma de sorteio ou em Comunicados Importantes.\n",
        "\n",
        "###Premiação\n",
        "O prêmio bruto corresponde a 43,35% da arrecadação. Dessa porcentagem:\n",
        "\n",
        "    35% são distribuídos entre os acertadores dos 6 números sorteados (Sena);\n",
        "\n",
        "    19% entre os acertadores de 5 números (Quina);\n",
        "\n",
        "    19% entre os acertadores de 4 números (Quadra);\n",
        "\n",
        "    22% ficam acumulados e são distribuídos aos acertadores dos 6 números nos concursos de final 0 ou 5.\n",
        "\n",
        "    5% ficam acumulados para a primeira faixa - sena - do último concurso do ano de final 0 ou 5 (Mega da Virada).\n",
        "\n",
        "\n",
        "###Concurso Especial - Mega da Virada\n",
        "O último concurso da Mega-Sena de final 0 ou 5 de cada ano civil, que tem a denominação comercial Mega da Virada, obedece às seguintes regras:\n",
        "\n",
        "**Prazo de comercialização:**\n",
        "\n",
        "Durante os meses de novembro e dezembro, com captação de apostas independente e concomitante com os demais concursos da modalidade, utilizando-se de volantes específicos (a CAIXA informará com antecedência a data do início das vendas e o número do concurso da Mega da Virada, que será sorteado no dia 31/12).\n",
        "\n",
        "**Distribuição do valor destinado ao pagamento dos prêmios:**\n",
        "\n",
        "62% - primeira faixa - seis acertos (sena);\n",
        "\n",
        "19% - segunda faixa - cinco acertos (quina);\n",
        "\n",
        "19% - terceira faixa - quatro acertos (quadra).\n",
        "\n",
        "**Composição da primeira faixa de premiação (sena):**\n",
        "\n",
        "- 62% do percentual destinado a prêmios, de acordo com a arrecadação do respectivo concurso;\n",
        "\n",
        "- O total acumulado para o último concurso de final zero ou cinco do ano civil;\n",
        "\n",
        "- O total acumulado para o concurso de final zero ou cinco;\n",
        "\n",
        "- O total acumulado na primeira faixa (sena) do concurso anterior, quando houver.\n",
        "\n",
        "**Critério de acumulação:**\n",
        "\n",
        "- Não existindo apostas premiadas com seis números (sena), o prêmio será rateado entre os acertadores de cinco números (quina);\n",
        "\n",
        "- Não existindo apostas premiadas com seis e cinco números, o prêmio será rateado entre os acertadores de quatro números (quadra);\n",
        "\n",
        "- Não existindo apostas premiadas em quaisquer faixas de premiação, os valores acumulam para o concurso seguinte, nas respectivas faixas.\n",
        "\n",
        "###Bolão\n",
        "\n",
        "O Bolão Caixa é a possibilidade que o apostador tem de realizar apostas em grupo. Basta preencher o campo próprio no volante ou solicitar ao atendente da lotérica. Você também pode comprar cotas de bolões organizados pelas Unidades Lotéricas. Neste caso, poderá ser cobrada uma Tarifa de Serviço adicional de até 35% do valor da cota. Na Mega-Sena, os bolões têm preço mínimo de R$ 10,00. Porém, cada cota não pode ser inferior a R$ 5,00. É possível realizar um bolão de no mínimo 2 e no máximo 100 cotas. É permitida a realização de no máximo 10 apostas por Bolão. Em caso de Bolão com mais de uma aposta, todas elas deverão conter a mesma quantidade de números de prognósticos.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "***Arrecadação***\n",
        "Quem joga na Mega-Sena tem milhões de motivos para apostar e milhões de brasileiros para ajudar. Parte do valor arrecadado com as apostas é repassada ao governo federal, que pode, então, realizar investimentos nas áreas de saúde, segurança, cultura e esporte, beneficiando toda a população.\n",
        "\n",
        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)\n",
        "\n",
        "*informações tiradas do site da [Mega Sena]( http://loterias.caixa.gov.br/wps/portal/loterias/landing/megasena/)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXNY5YKtymbu"
      },
      "source": [
        "##Dados Mega Sena \n",
        "\n",
        "os dados vão de 11/03/1996 a 27/11/2021\n",
        "\n",
        "https://drive.google.com/file/d/1K-1_EuvTL-AbzvNMQA-o-ZQusMn5Qyss/view?usp=sharing\n",
        "\n",
        "Infor\n",
        "\n",
        "**Concurso**\t-- Numero do concurso\n",
        "\n",
        "**Local**\t-- Local onde o sorteio foi realizado (não confundir com local do ganhador)\n",
        "\n",
        "**Data do Sorteio** -- Data em que o sorteio foi realizado\n",
        "\n",
        "**Coluna 1**\t-- 1º Dezena sorteada\n",
        "\n",
        "**Coluna 2**\t-- 2º Dezena sorteada\n",
        "\n",
        "**Coluna 3**\t-- 3º Dezena sorteada\n",
        "\n",
        "**Coluna 4**\t-- 4º Dezena sorteada\n",
        "\n",
        "**Coluna 5**\t-- 5º Dezena sorteada\n",
        "\n",
        "**Coluna 6**\t-- 6º Dezena sorteada\n",
        "\n",
        "**Ganhadores Faixa 1** -- Quantas pessoas ganharam o prêmio principal (mega)\n",
        "\n",
        "**Ganhadores Faixa 2**\t-- Quanstas pessoas ganharam a quina \n",
        "\n",
        "**Ganhadores Faixa 3**\t-- Quantas pessoas ganharam a quadra\n",
        "\n",
        "**Rateio Faixa 1**\t-- Quanto cada pessoa recebeu da mega\n",
        "\n",
        "**Rateio Faixa 2**\t-- Quanto cada pessoa recebeu na quina \n",
        "\n",
        "**Rateio Faixa 3**\t-- Quanto cada pessoa recebeu na quadra \n",
        "\n",
        "**Cidade**\t-- Estado da pessoa ganhadora\n",
        "\n",
        "**Valor Arrecadado**\t-- Valor arrecadado para o proximo concurso\n",
        "\n",
        "**Estimativa para o próximo concurso**\t\n",
        "\n",
        "**Valor Acumulado Próximo Concurso**\t\n",
        "\n",
        "**Acumulado** -- O sorteio estava acumulado?\n",
        "\n",
        "**Sorteio Especial** -- É um sorteio da mega da virada?\n",
        "\n",
        "**Observação**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0x9b4KQgkhkD"
      },
      "source": [
        "\n",
        "\n",
        "# Perguntas que queremos responder:\n",
        "\n",
        "* Qual o estado com maior numero de ganhadores da Mega Sena? **(fazendo)**\n",
        "* Em qual cidade foi sorteado o maior valor? **(fazendo)**\n",
        "---\n",
        "* Quais os 6 números mais sorteados? (não necessariamentes na mesma aposta) **Respondida)**\n",
        "* Quais os 6 números menos sorteados? (não necessariamentes na mesma aposta) **(Respondida)**\n",
        "* Existe algum numero que nunca saiu no sorteio comum? E na Mega da Virada? **(Respondida)**\n",
        "---\n",
        "* Qual a média do premio pago para cada faixa (sena, quina e quadra)? **(respondida)**\n",
        "* Qual a média do premio pago em jogos especiais (mega da virada)? **(respondida)**\n",
        "---\n",
        "* Em média por quantas rodadas um jogo fica acumulado?\n",
        "* Qual foi o jogo com o maior numero de rodadas acumuladas? Quantas rodadas?\n",
        "---\n",
        "* Quantos jogos é necessário fazer para ter certeza da vitória? \n",
        "* Quantos anos em média uma pessoa demoraria para ganhar jogando sempre os mesmos números?\n",
        "* Qual a média do valor gasto em apostas por ano? \n",
        "---\n",
        "* Quantos reais já foram distribuidos em premios até hoje? **(respondida)**\n",
        "* Quantos reais em impostos já foram pagos atravez da Mega Sena? **(respondida)**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtrpcEgw5VPI"
      },
      "source": [
        "#importando bibliotecas\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "import string\n",
        "import heapq  "
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3t1ol6V6yHU"
      },
      "source": [
        "#carregando base de dados\n",
        "#a base foi retirada do site oficial da caixa, com informações sobre a mega sena\n",
        "url = 'https://drive.google.com/file/d/1K-1_EuvTL-AbzvNMQA-o-ZQusMn5Qyss/view?usp=sharing'\n",
        "path = 'https://drive.google.com/uc?export=download&id='+ url.split('/')[-2]\n",
        "\n",
        "megaSena = pd.read_csv(path)\n"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "_x1BcbXj8Enq",
        "outputId": "8e5f4b6b-438f-4f75-f146-fef5c517948d"
      },
      "source": [
        "megaSena.head()\n",
        "#Não vale a pena fazer dropna para tirar os NaN da Observação (existem poucos concursos com observações, geralmente quando ganhador principal fez o jogo Online)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Concurso</th>\n",
              "      <th>Local</th>\n",
              "      <th>Data do Sorteio</th>\n",
              "      <th>Coluna 1</th>\n",
              "      <th>Coluna 2</th>\n",
              "      <th>Coluna 3</th>\n",
              "      <th>Coluna 4</th>\n",
              "      <th>Coluna 5</th>\n",
              "      <th>Coluna 6</th>\n",
              "      <th>Ganhadores Faixa 1</th>\n",
              "      <th>Ganhadores Faixa 2</th>\n",
              "      <th>Ganhadores Faixa 3</th>\n",
              "      <th>Rateio Faixa 1</th>\n",
              "      <th>Rateio Faixa 2</th>\n",
              "      <th>Rateio Faixa 3</th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Valor Arrecadado</th>\n",
              "      <th>Estimativa para o próximo concurso</th>\n",
              "      <th>Valor Acumulado Próximo Concurso</th>\n",
              "      <th>Acumulado</th>\n",
              "      <th>Sorteio Especial</th>\n",
              "      <th>Observação</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>11/03/1996</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39158.92</td>\n",
              "      <td>330.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Belo Horizonte, MG</td>\n",
              "      <td>18/03/1996</td>\n",
              "      <td>9.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4488.0</td>\n",
              "      <td>2307162.23</td>\n",
              "      <td>14424.02</td>\n",
              "      <td>208.91</td>\n",
              "      <td>PR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NAO</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>25/03/1996</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>4261.0</td>\n",
              "      <td>391192.51</td>\n",
              "      <td>10515.93</td>\n",
              "      <td>153.01</td>\n",
              "      <td>RN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NAO</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Santo André, SP</td>\n",
              "      <td>01/04/1996</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3311.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15322.24</td>\n",
              "      <td>180.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>08/04/1996</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>5399.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5318.10</td>\n",
              "      <td>96.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Concurso               Local  ... Sorteio Especial  Observação\n",
              "0        1        Brasília, DF  ...              SIM         NaN\n",
              "1        2  Belo Horizonte, MG  ...              SIM         NaN\n",
              "2        3        Brasília, DF  ...              SIM         NaN\n",
              "3        4     Santo André, SP  ...              SIM         NaN\n",
              "4        5        Brasília, DF  ...              SIM         NaN\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "FabtTZFv9hhv",
        "outputId": "131dc43a-304a-4fb3-9559-dc10eda358a8"
      },
      "source": [
        "#renomeando colunas\n",
        "megaSena.columns = ['concurso', 'localSorteio', 'dataSorteio', 'num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'ganhadorSena', 'ganhadorQuina', 'ganhadorQuadra', 'rateioSena', 'rateioQuina', 'rateioQuadra', 'ufGanhadorSena', 'valorArrecadado', 'estimativaPremioProximoConcurso', 'acumuladoProximoConcurso', 'estavaAcumulado', 'sorteioEspecial', 'obs']\n",
        "megaSena.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\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>concurso</th>\n",
              "      <th>localSorteio</th>\n",
              "      <th>dataSorteio</th>\n",
              "      <th>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "      <th>num4</th>\n",
              "      <th>num5</th>\n",
              "      <th>num6</th>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th>ganhadorQuina</th>\n",
              "      <th>ganhadorQuadra</th>\n",
              "      <th>rateioSena</th>\n",
              "      <th>rateioQuina</th>\n",
              "      <th>rateioQuadra</th>\n",
              "      <th>ufGanhadorSena</th>\n",
              "      <th>valorArrecadado</th>\n",
              "      <th>estimativaPremioProximoConcurso</th>\n",
              "      <th>acumuladoProximoConcurso</th>\n",
              "      <th>estavaAcumulado</th>\n",
              "      <th>sorteioEspecial</th>\n",
              "      <th>obs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>11/03/1996</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39158.92</td>\n",
              "      <td>330.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Belo Horizonte, MG</td>\n",
              "      <td>18/03/1996</td>\n",
              "      <td>9.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4488.0</td>\n",
              "      <td>2307162.23</td>\n",
              "      <td>14424.02</td>\n",
              "      <td>208.91</td>\n",
              "      <td>PR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NAO</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>25/03/1996</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>4261.0</td>\n",
              "      <td>391192.51</td>\n",
              "      <td>10515.93</td>\n",
              "      <td>153.01</td>\n",
              "      <td>RN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NAO</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Santo André, SP</td>\n",
              "      <td>01/04/1996</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3311.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15322.24</td>\n",
              "      <td>180.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Brasília, DF</td>\n",
              "      <td>08/04/1996</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>5399.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5318.10</td>\n",
              "      <td>96.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "      <td>SIM</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  concurso        localSorteio dataSorteio  ...  estavaAcumulado  sorteioEspecial  obs\n",
              "0        1        Brasília, DF  11/03/1996  ...              SIM              SIM  NaN\n",
              "1        2  Belo Horizonte, MG  18/03/1996  ...              NAO              SIM  NaN\n",
              "2        3        Brasília, DF  25/03/1996  ...              NAO              SIM  NaN\n",
              "3        4     Santo André, SP  01/04/1996  ...              SIM              SIM  NaN\n",
              "4        5        Brasília, DF  08/04/1996  ...              SIM              SIM  NaN\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9mAvUmK9j0y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "d3848039-713f-4806-883d-ab1e8fa007b5"
      },
      "source": [
        "megaSena.describe()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "      <th>num4</th>\n",
              "      <th>num5</th>\n",
              "      <th>num6</th>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th>ganhadorQuina</th>\n",
              "      <th>ganhadorQuadra</th>\n",
              "      <th>rateioSena</th>\n",
              "      <th>rateioQuina</th>\n",
              "      <th>rateioQuadra</th>\n",
              "      <th>valorArrecadado</th>\n",
              "      <th>estimativaPremioProximoConcurso</th>\n",
              "      <th>acumuladoProximoConcurso</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2.432000e+03</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2432.000000</td>\n",
              "      <td>2.432000e+03</td>\n",
              "      <td>2.432000e+03</td>\n",
              "      <td>2.432000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.615132</td>\n",
              "      <td>17.300987</td>\n",
              "      <td>26.289062</td>\n",
              "      <td>35.022204</td>\n",
              "      <td>43.518503</td>\n",
              "      <td>52.203947</td>\n",
              "      <td>0.346628</td>\n",
              "      <td>111.515214</td>\n",
              "      <td>7142.204770</td>\n",
              "      <td>4.904852e+06</td>\n",
              "      <td>26124.479708</td>\n",
              "      <td>451.881493</td>\n",
              "      <td>2.841470e+07</td>\n",
              "      <td>1.554293e+07</td>\n",
              "      <td>2.022658e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.004487</td>\n",
              "      <td>9.120242</td>\n",
              "      <td>9.966538</td>\n",
              "      <td>9.791676</td>\n",
              "      <td>9.009460</td>\n",
              "      <td>6.945603</td>\n",
              "      <td>1.333490</td>\n",
              "      <td>231.349666</td>\n",
              "      <td>10667.955317</td>\n",
              "      <td>1.537624e+07</td>\n",
              "      <td>17508.151356</td>\n",
              "      <td>294.698329</td>\n",
              "      <td>6.201162e+07</td>\n",
              "      <td>2.815787e+07</td>\n",
              "      <td>2.460358e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>683.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>122.530000</td>\n",
              "      <td>69.550000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>3678.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>13948.810000</td>\n",
              "      <td>218.495000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>5326.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>21207.470000</td>\n",
              "      <td>354.700000</td>\n",
              "      <td>2.369248e+07</td>\n",
              "      <td>3.900000e+06</td>\n",
              "      <td>8.068430e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>117.250000</td>\n",
              "      <td>7655.750000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>33670.382500</td>\n",
              "      <td>635.372500</td>\n",
              "      <td>3.974476e+07</td>\n",
              "      <td>2.300000e+07</td>\n",
              "      <td>3.769411e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>7688.000000</td>\n",
              "      <td>303857.000000</td>\n",
              "      <td>2.894209e+08</td>\n",
              "      <td>169338.140000</td>\n",
              "      <td>1724.830000</td>\n",
              "      <td>1.175719e+09</td>\n",
              "      <td>3.000000e+08</td>\n",
              "      <td>1.390996e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              num1  ...  acumuladoProximoConcurso\n",
              "count  2432.000000  ...              2.432000e+03\n",
              "mean      8.615132  ...              2.022658e+07\n",
              "std       7.004487  ...              2.460358e+07\n",
              "min       1.000000  ...              0.000000e+00\n",
              "25%       3.000000  ...              0.000000e+00\n",
              "50%       7.000000  ...              8.068430e+06\n",
              "75%      12.000000  ...              3.769411e+07\n",
              "max      50.000000  ...              1.390996e+08\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nffM-_EhNtV4"
      },
      "source": [
        "### Qual o estado com maior numero de ganhadores da Mega Sena?\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_qqr8S79KUqs",
        "outputId": "ec9e51e0-cc88-45be-fb84-ab89251f8a37"
      },
      "source": [
        "local = megaSena[['localSorteio', 'concurso']]\n",
        "local.groupby('localSorteio').count().sort_values(by='concurso', ascending=False)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concurso</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>localSorteio</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SÃO PAULO, SP</th>\n",
              "      <td>401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OSASCO, SP</th>\n",
              "      <td>101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brasília, DF</th>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BRASÍLIA, DF</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IPAMERI, GO</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JAGUAQUARA, BA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JACUPIRANGA, SP</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JACOBINA, BA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JACIARA, MT</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Águas de Lindóia, SP</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>799 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      concurso\n",
              "localSorteio                  \n",
              "SÃO PAULO, SP              401\n",
              "OSASCO, SP                 101\n",
              "Brasília, DF               100\n",
              "BRASÍLIA, DF                29\n",
              "IPAMERI, GO                 23\n",
              "...                        ...\n",
              "JAGUAQUARA, BA               1\n",
              "JACUPIRANGA, SP              1\n",
              "JACOBINA, BA                 1\n",
              "JACIARA, MT                  1\n",
              "Águas de Lindóia, SP         1\n",
              "\n",
              "[799 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3G7ojJtjNmYT"
      },
      "source": [
        "### Qual o concurso com maior número de ganhadores?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "25BMFQ4RNqL4",
        "outputId": "10e91de3-1168-4d6d-a48b-a12a8b7f27ca"
      },
      "source": [
        "num_ganhadores = megaSena[['ganhadorSena', 'concurso']]\n",
        "num_ganhadores.groupby('ganhadorSena').count().sort_values(by='ganhadorSena', ascending=False)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concurso</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1877</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              concurso\n",
              "ganhadorSena          \n",
              "52.0                 1\n",
              "17.0                 1\n",
              "15.0                 1\n",
              "7.0                  1\n",
              "6.0                  2\n",
              "5.0                  3\n",
              "4.0                 12\n",
              "3.0                 23\n",
              "2.0                 97\n",
              "1.0                414\n",
              "0.0               1877"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlqI_7o-yr5E"
      },
      "source": [
        "###Em média quantos jogos ficaram acumulados por mais de uma rodada, no mínimo? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afBq8YyxzQJo",
        "outputId": "219b62b6-961a-493a-875b-980d38e5a756"
      },
      "source": [
        "quant_acumulado = megaSena.query(\"estavaAcumulado == 'SIM'\")\n",
        "quant_acumulado2 = quant_acumulado.query(\"acumuladoProximoConcurso > 0\")\n",
        "\n",
        "contagem_sim = quant_acumulado2.shape[0]\n",
        "contagem_total = megaSena.shape[0]\n",
        "\n",
        "print(\"A média de jogos que acumularam por mais de uma semana é de:\", contagem_sim/contagem_total)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A média de jogos que acumularam por mais de uma semana é de: 0.4459515002055076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOWY3Mu5MUni"
      },
      "source": [
        "### Qual foi o maior valor já sorteado na Sena e em qual cidade foi sorteado?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuHu8FjaX0ev"
      },
      "source": [
        "#sorteiomaximo = megaSena.sort_values(by=\"Rateio Faixa 1\", ascending=False)\n",
        "#print('O maior prêmio da sena foi de R$', megaSena['rateioSena'].max(), '\\nE o ganhador(a) era de', megaSena[['localSorteio']].loc[megaSena['rateioSena'] == valormax])"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "xUQKm-VqQFf1",
        "outputId": "af026f0c-c51d-4433-b9e6-4e26f5558054"
      },
      "source": [
        "megaSena['num1'].hist()\n",
        "megaSena['num2'].hist()\n",
        "megaSena['num3'].hist()\n",
        "megaSena['num4'].hist()\n",
        "megaSena['num5'].hist()\n",
        "megaSena['num6'].hist()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb0b11ac50>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75FokNx0hViQ"
      },
      "source": [
        "### Quantos reais já foram distribuidos em premios até hoje?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Sg7AfgpCT8Fn",
        "outputId": "7125f412-d226-4568-dad5-010dfc517970"
      },
      "source": [
        "premios = megaSena[['concurso', 'ganhadorSena', 'ganhadorQuina', 'ganhadorQuadra', 'rateioSena', 'rateioQuina', 'rateioQuadra', 'ufGanhadorSena', 'valorArrecadado', 'sorteioEspecial']]\n",
        "premios.head()\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concurso</th>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th>ganhadorQuina</th>\n",
              "      <th>ganhadorQuadra</th>\n",
              "      <th>rateioSena</th>\n",
              "      <th>rateioQuina</th>\n",
              "      <th>rateioQuadra</th>\n",
              "      <th>ufGanhadorSena</th>\n",
              "      <th>valorArrecadado</th>\n",
              "      <th>sorteioEspecial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39158.92</td>\n",
              "      <td>330.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4488.0</td>\n",
              "      <td>2307162.23</td>\n",
              "      <td>14424.02</td>\n",
              "      <td>208.91</td>\n",
              "      <td>PR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>4261.0</td>\n",
              "      <td>391192.51</td>\n",
              "      <td>10515.93</td>\n",
              "      <td>153.01</td>\n",
              "      <td>RN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3311.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15322.24</td>\n",
              "      <td>180.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>5399.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5318.10</td>\n",
              "      <td>96.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  concurso  ganhadorSena  ...  valorArrecadado  sorteioEspecial\n",
              "0        1           0.0  ...              0.0              SIM\n",
              "1        2           1.0  ...              0.0              SIM\n",
              "2        3           2.0  ...              0.0              SIM\n",
              "3        4           0.0  ...              0.0              SIM\n",
              "4        5           0.0  ...              0.0              SIM\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgVJwDc1V4Pa",
        "outputId": "b29a7443-5fad-476e-b897-01ff13da5704"
      },
      "source": [
        "premioSena = (premios['rateioSena']) * (premios['ganhadorSena'])\n",
        "premioQuina = (premios['rateioQuina']) * (premios['ganhadorQuina'])\n",
        "premioQuadra = (premios['rateioQuadra']) * (premios['ganhadorQuadra'])\n",
        "premioQuadra"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        665703.36\n",
              "1        937588.08\n",
              "2        651975.61\n",
              "3        597569.28\n",
              "4        521165.47\n",
              "           ...    \n",
              "2428    2809265.10\n",
              "2429    5100378.43\n",
              "2430    2339125.92\n",
              "2431    3025880.84\n",
              "2432           NaN\n",
              "Length: 2433, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Cw3Tbg_KWT5y",
        "outputId": "ba849bdc-6ac5-43de-abd9-848d48d5a74f"
      },
      "source": [
        "premios = [premioQuadra, premioQuina, premioSena]\n",
        "premios = pd.DataFrame(list(zip(premioSena, premioQuina, premioQuadra)), columns=['premioSena', 'premioQuina', 'premioQuadra'])\n",
        "premios = premios.dropna()\n",
        "premios.head()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>premioSena</th>\n",
              "      <th>premioQuina</th>\n",
              "      <th>premioQuadra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00</td>\n",
              "      <td>665701.64</td>\n",
              "      <td>665703.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2307162.23</td>\n",
              "      <td>937561.30</td>\n",
              "      <td>937588.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>782385.02</td>\n",
              "      <td>651987.66</td>\n",
              "      <td>651975.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>597567.36</td>\n",
              "      <td>597569.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>521173.80</td>\n",
              "      <td>521165.47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   premioSena  premioQuina  premioQuadra\n",
              "0        0.00    665701.64     665703.36\n",
              "1  2307162.23    937561.30     937588.08\n",
              "2   782385.02    651987.66     651975.61\n",
              "3        0.00    597567.36     597569.28\n",
              "4        0.00    521173.80     521165.47"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf3D5v6DeFLl",
        "outputId": "515c79de-ba3d-4f50-bc2a-608037f257e2"
      },
      "source": [
        "len(premios['premioSena'])"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2432"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRsA7v0Hf9kT",
        "outputId": "10abe474-4ac5-4dbd-ae1c-53545969c39c"
      },
      "source": [
        "somatoriaSena = round(premios['premioSena'].sum(), 2)\n",
        "somatoriaQuina = round(premios['premioQuina'].sum(), 2)\n",
        "somatoriaQuadra = round(premios['premioQuadra'].sum(), 2)\n",
        "somatoriaTotal = somatoriaSena + somatoriaQuina + somatoriaQuadra\n",
        "\n",
        "porcentagemSena = round((somatoriaSena*100)/somatoriaTotal, 5)\n",
        "porcentagemQuina = round((somatoriaQuina*100)/somatoriaTotal,5)\n",
        "porecentagemQuadra = round((somatoriaQuadra*100)/somatoriaTotal,5)\n",
        "\n",
        "porC= porcentagemSena + porcentagemQuina + porecentagemQuadra\n",
        "\n",
        "print(somatoriaSena, somatoriaQuina, somatoriaQuadra)\n",
        "print('Somatoria das porcentagens:',porC)\n",
        "print('A Mega Sena já distribuiu ', 'R$',somatoriaTotal, '\\nSendo que a Sena é responsalvel por ', porcentagemSena, '\\nA Quina por ', porcentagemQuina, ' e a Quadra por ', porecentagemQuadra)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16405460035.45 5342332728.0 7063971967.57\n",
            "Somatoria das porcentagens: 100.0\n",
            "A Mega Sena já distribuiu  R$ 28811764731.02 \n",
            "Sendo que a Sena é responsalvel por  56.94014 \n",
            "A Quina por  18.54219  e a Quadra por  24.51767\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLEYJ5fqQKW9"
      },
      "source": [
        "A Mega Sena desde a sua criação já distribuiu de R\\$ 28.811.764.731,02 ***(vinte e oito bilhões oitocentos e onze milhões setecentos e sessenta e quatro mil setecentos e trinta e um reais e dois centavos)*** até a data 27 de Novembro de 2021desconsiderando os impostos, se considerado os 30% de impostos a Mega Sena distribuiu R\\$ 20.168.235.311,71 ***(vinte bilhões cento e sessenta e oito milhões duzentos e trinta e cinco mil trezentos e onze reais e setenta e um centavos)*** em premios\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDq9u_YjQC37"
      },
      "source": [
        "### Quanto em imposto já foi pago na mega sena?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUpHS3kCkbGQ",
        "outputId": "f61bf22d-923d-449d-e8c9-69ff6599db79"
      },
      "source": [
        "impostoRecolhido = somatoriaTotal*.3 #É recolhido 30% do valor pago no premio para o Imposto de Renda\n",
        "premioRecebido = somatoriaTotal - impostoRecolhido\n",
        "\n",
        "print(round(impostoRecolhido, 2), round(premioRecebido, 2))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8643529419.31 20168235311.71\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i31JeyL57Um1",
        "outputId": "0126f06b-c532-4291-fa3c-92df4190f12d"
      },
      "source": [
        "impostoAno = round(impostoRecolhido/25, 2)\n",
        "premioAno = round(premioRecebido/25, 2)\n",
        "\n",
        "print(impostoAno, premioAno)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "345741176.77 806729412.47\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4WbWMdDgpJz"
      },
      "source": [
        "Se fosse distribuido integralmente (sem desconto de imposto) para um unica pessoa, ela seria a **13º Pessoa Mais rica do Brasil** e a **622º Mais rica do mundo em 2021**, com **US\\$ 5,02 bilhões**. Caso fosse descontado o imposto esta pessoa seria a ***16º Mais rica do Brasil*** e a ***859º Mais rica do Mundo***, com US\\$ 3.56 bilhões\n",
        "\n",
        "Fonte:<https://comoinvestir.thecap.com.br/quem-sao-os-bilionarios-brasileiros-de-2021/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kzHVJDIcYdl"
      },
      "source": [
        "### Qual a média do premio pago para cada faixa (sena, quina e quadra)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2PdMvodcXBJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "827ba7c4-5b53-4b13-ed4b-51bb56cba19d"
      },
      "source": [
        "premios = megaSena[['concurso', 'ganhadorSena', 'ganhadorQuina', 'ganhadorQuadra', 'rateioSena', 'rateioQuina', 'rateioQuadra', 'ufGanhadorSena', 'valorArrecadado', 'sorteioEspecial']]\n",
        "premios.head()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concurso</th>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th>ganhadorQuina</th>\n",
              "      <th>ganhadorQuadra</th>\n",
              "      <th>rateioSena</th>\n",
              "      <th>rateioQuina</th>\n",
              "      <th>rateioQuadra</th>\n",
              "      <th>ufGanhadorSena</th>\n",
              "      <th>valorArrecadado</th>\n",
              "      <th>sorteioEspecial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39158.92</td>\n",
              "      <td>330.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4488.0</td>\n",
              "      <td>2307162.23</td>\n",
              "      <td>14424.02</td>\n",
              "      <td>208.91</td>\n",
              "      <td>PR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>4261.0</td>\n",
              "      <td>391192.51</td>\n",
              "      <td>10515.93</td>\n",
              "      <td>153.01</td>\n",
              "      <td>RN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3311.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15322.24</td>\n",
              "      <td>180.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>5399.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5318.10</td>\n",
              "      <td>96.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  concurso  ganhadorSena  ...  valorArrecadado  sorteioEspecial\n",
              "0        1           0.0  ...              0.0              SIM\n",
              "1        2           1.0  ...              0.0              SIM\n",
              "2        3           2.0  ...              0.0              SIM\n",
              "3        4           0.0  ...              0.0              SIM\n",
              "4        5           0.0  ...              0.0              SIM\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1htalRdAz25b",
        "outputId": "7202d588-5ea1-402e-9204-9ad8ffac8c7c"
      },
      "source": [
        "mediaSena = premios['rateioSena'].mean()\n",
        "mediaQuina = premios['rateioQuina'].mean()\n",
        "mediaQuadra = premios['rateioQuadra'].mean()\n",
        "\n",
        "print('O premio medio da Sena é: R$ ',round(mediaSena, 2),'\\nO premio medio da Quina é: R$ ', round(mediaQuina, 2),'\\nO premio medio da Quina é: R$ ', round(mediaQuadra, 2))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O premio medio da Sena é: R$  4904851.81 \n",
            "O premio medio da Quina é: R$  26124.48 \n",
            "O premio medio da Quina é: R$  451.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKzCxNi_QS91"
      },
      "source": [
        "### Qual a média do premio pago em jogos especiais (ex. mega da virada)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fWGb7G9T-X6R",
        "outputId": "20fbb867-422e-4b8f-a321-62d18d063859"
      },
      "source": [
        "#df_mask=df['col_name']=='specific_value'\n",
        "\n",
        "mascaraFiltro = premios['sorteioEspecial'] == 'SIM'\n",
        "concursoEspecial = premios[mascaraFiltro]\n",
        "\n",
        "concursoEspecial.head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>concurso</th>\n",
              "      <th>ganhadorSena</th>\n",
              "      <th>ganhadorQuina</th>\n",
              "      <th>ganhadorQuadra</th>\n",
              "      <th>rateioSena</th>\n",
              "      <th>rateioQuina</th>\n",
              "      <th>rateioQuadra</th>\n",
              "      <th>ufGanhadorSena</th>\n",
              "      <th>valorArrecadado</th>\n",
              "      <th>sorteioEspecial</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39158.92</td>\n",
              "      <td>330.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>4488.0</td>\n",
              "      <td>2307162.23</td>\n",
              "      <td>14424.02</td>\n",
              "      <td>208.91</td>\n",
              "      <td>PR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>4261.0</td>\n",
              "      <td>391192.51</td>\n",
              "      <td>10515.93</td>\n",
              "      <td>153.01</td>\n",
              "      <td>RN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3311.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15322.24</td>\n",
              "      <td>180.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>5399.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5318.10</td>\n",
              "      <td>96.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SIM</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  concurso  ganhadorSena  ...  valorArrecadado  sorteioEspecial\n",
              "0        1           0.0  ...              0.0              SIM\n",
              "1        2           1.0  ...              0.0              SIM\n",
              "2        3           2.0  ...              0.0              SIM\n",
              "3        4           0.0  ...              0.0              SIM\n",
              "4        5           0.0  ...              0.0              SIM\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pL2TsfXfCjTv",
        "outputId": "11bdf9b3-1f0c-44d6-c2cb-8517aafb9341"
      },
      "source": [
        "mediaSenaEspecial = concursoEspecial['rateioSena'].mean()\n",
        "mediaQuinaEspecial = concursoEspecial['rateioQuina'].mean()\n",
        "mediaQuadraEspecial = concursoEspecial['rateioQuadra'].mean()\n",
        "\n",
        "print('O premio medio da Sena Especial é: R$ ',round(mediaSenaEspecial, 2),'\\nO premio medio da Quina Especial é: R$ ', round(mediaQuinaEspecial, 2),'\\nO premio medio da Quadra Especial é: R$ ', round(mediaQuadraEspecial, 2))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O premio medio da Sena Especial é: R$  4804802.61 \n",
            "O premio medio da Quina Especial é: R$  26116.56 \n",
            "O premio medio da Quadra Especial é: R$  451.52\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flnQfTFCEUbW",
        "outputId": "9cdcd649-1a42-46cd-fdde-d3f4a7a7773a"
      },
      "source": [
        "diferencaSena = mediaSenaEspecial - mediaSena\n",
        "diferencaQuina = mediaQuinaEspecial - mediaQuina\n",
        "diferencaQuadra = mediaQuadraEspecial - mediaQuadra\n",
        "\n",
        "print('A diferença entro o sorteio especial e o comum da Sena é: R$ ',round(diferencaSena, 2),'\\nA diferença entro o sorteio especial e o comum da Quina é: R$ ', round(diferencaQuina, 2),'\\nA diferença entro o sorteio especial e o comum da Quadra é: R$ ', round(diferencaQuadra, 2))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A diferença entro o sorteio especial e o comum da Sena é: R$  -100049.2 \n",
            "A diferença entro o sorteio especial e o comum da Quina é: R$  -7.92 \n",
            "A diferença entro o sorteio especial e o comum da Quadra é: R$  -0.36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKi2nZCVFttE"
      },
      "source": [
        "A diferença entre os premios dos sorteios comuns VS dos sorteios especiais é bem pequena, na Sena é onde temos a maior diferença (R$ 100.049,20)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QPGES7mGn7v"
      },
      "source": [
        "#Tentativa de selecionar apenas os sorteios da Mega da virada (a partir de 31/12/2008, sendo realizado todo dia 31/12)\n",
        "\n",
        "\n",
        "#'(31)(\\/)(12)(\\/)(20)((0[8-9])|[1-2][0-9])'\n",
        "#mascaraFiltro = megaSena['dataSorteio'] == '31/12/2008'  '(31)(\\/)(12)(\\/)(20)[1-2][0-9])'\n",
        "#megaVirada = megaSena[mascaraFiltro].filter(r'(31)(\\/)(12)(\\/)(20)((0[8-9])|[1-2][0-9])')\n"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiN2DoUkbQX8"
      },
      "source": [
        "###Quais os 6 números mais sorteados? (não necessariamentes na mesma aposta)\n",
        "###Quais os 6 números menos sorteados? (não necessariamentes na mesma aposta)\n",
        "###Existe algum numero que nunca saiu no sorteio comum?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4p1I2FOlbXt6",
        "outputId": "e848662d-5af1-4499-df55-6290a203178a"
      },
      "source": [
        "numerosSorteados = megaSena[['num1', 'num2', 'num3', 'num4', 'num5', 'num6']]\n",
        "numerosSorteados.head()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "      <th>num4</th>\n",
              "      <th>num5</th>\n",
              "      <th>num6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>49.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   num1  num2  num3  num4  num5  num6\n",
              "0   4.0   5.0  30.0  33.0  41.0  52.0\n",
              "1   9.0  37.0  39.0  41.0  43.0  49.0\n",
              "2  10.0  11.0  29.0  30.0  36.0  47.0\n",
              "3   1.0   5.0   6.0  27.0  42.0  59.0\n",
              "4   1.0   2.0   6.0  16.0  19.0  46.0"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Io4pj1zXRAB",
        "outputId": "2d7f6deb-df89-488c-9f2c-5ea6622c0248"
      },
      "source": [
        "numerosSorteados.info()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2433 entries, 0 to 2432\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   num1    2432 non-null   float64\n",
            " 1   num2    2432 non-null   float64\n",
            " 2   num3    2432 non-null   float64\n",
            " 3   num4    2432 non-null   float64\n",
            " 4   num5    2432 non-null   float64\n",
            " 5   num6    2432 non-null   float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 114.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "EcIPseDEjeVT",
        "outputId": "a561a1b0-86c3-4990-8b85-b5c3e4d2573c"
      },
      "source": [
        "numerosSorteados.hist()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0b1d5dd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0ab15e50>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0aa9b490>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0aa52a90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0aa140d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb0a9cb6d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cD3wB+Kikx9LrVGApcJKkDWStHXrhKVaXABdJ2kh2HnV5weUpNcd5V+vqWHcXE2ZmFdcVNQIzM2uf0j2Yptb06dOjv7+/6GK0ZOfOnUyZMqXoYrSsm9djzZo1OyLi0KLLUa+yxn03x8B4enWd1q9f31DMlzoR9Pf3s3r16qKL0ZLBwUEGBgaKLkbLunk9JD1TdBkaUda47+YYGE+vrtMJJ5zQUMz71JCZWcWVukbQzfqX3A3A4nnDLEzDE9m0tJ7uhcyqrb/O/Wk071/jc43AzKzinAjMzCrOicDMrOKcCMzMKs6JwMys4txqyKxB6Uliq4EtEXG6pNlkD5E5BFgDfCEi/ijpbcANwF8AzwOfjYhNBRW78sZqbTRRq76qtDRyjcCscfU+pvBc4MU0/cq0nFnptFwj8NFRfpppH12VI5ayqHlM4f8g621y5DGF/yEtsoLsyVrXkj2W8etp+q3AdyUp3NOjlUweNQIfHVmVNPKYwt2PnkzzX07Lm5VKSzUCHx1ZldQ+plDSQM6fvQhYBNDX18fg4GCeH5+LoaGhUpRr8bzhiReqU9/+e/+8Mqxvo4aGhhp+T6unhkaOjg5I43UfHUkaOTraUfuB3bBD1GMkuCYKtFZ16vcpyz+Bgh0PfDI9YGU/4B3A1cBBkianuJ8JbEnLbwFmAZslTQYOJDst+hYRsYzsgevMnz8/ytgRWlk6aKu3y5Z6LJ43zOVrx/83uGnBQG7f1SnN7KdNJ4J2HR11ww5Rj4U1fQ3tLdBatnZnw29p5rpCWf4JFCkiLiV7jjAp5i+OiAWS/o7sMYU3sedjCkcey/irNP9+14CtjFr5D9W2oyOzLnMJcJOkbwGP8uZjCpcDf5seX/gC2TNurUazHchZvppOBD46siqLiEFgMA0/DRwzxjK7gM90tGBmTWjHfQTjPcR5OXBImn4RsKQN321mZg3K5eS1j47MzLqXu5gwMxtHVW7ydBcTZmYV50RgZlZxPjU0ATdvM7Ne50RQQc0kt8XzhhnIvyhmVgI+NWRmVnFOBGZmFedTQ2aWC19P616uEZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4FZnSTNkvSApCclPSHpgjR9mqT7JG1Ifw9O0yXpGkkbJT0u6ehi18BsbE0nAu8UVkHDwOKImAscB5wnaS7ZszVWRsQcYCVvPmvjFGBOei0Cru18kc0m1kqNwDuFVUpEbI2IR9Lwq8A64HDgDGBFWmwF8Kk0fAZwQ2QeInuM64wOF9tsQq08qnIrsDUNvyqpdqcYSIutIHtgzSXU7BTAQ5IOkjQjfY5ZV5HUDxwFrAL6auJ4G9CXhg8Hnq152+Y07S0xL2kR2QESfX19DA4OtqPYLRkaGtpruRbPG+5cYXLSt3/+5S562w0NDTX8nlzuLM5zpyjbDtFqkLQj0IrQt3/xAV4WkqYCtwEXRsQrknbPi4iQ1PCzuCNiGbAMYP78+TEwMJBTafMzODjI3sq1sAvvLF48b5jL1+bbwcKmBQO5fl6jmtlPW/4F8t4p2rlDNHcLfGs/UTsCrQiL5w1zZgn/OXWapH3I4v3GiLg9TX5upHabTv1sT9O3ALNq3j4zTTMrlZb+Q3mnsCpRdpSzHFgXEVfUzLoTOBtYmv7+uGb6+ZJuAo4FXu6WU6FjHTQtnjfclUf9ndaNj7dspdXQRDsFvHWn+GJqPXQcXbRTmCXHA18APirpsfQ6lSwBnCRpA/CxNA5wD/A0sBH4G+BLBZTZbEKt1AhGdoq1kh5L075KthPcIulc4BngzDTvHuBUsp3iNeCcFr7brOMi4kFA48w+cYzlAzivrYUyy0ErrYa8U5iZ9QDfWWxmVnHd35zFOqYbL4KZ2cRcIzAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6s4JwIzs4pz81GzHtZcR4tWNV2bCBzgZtYrmv1/ltd9Oj41ZGZWcV1bI7Du4LuRzcrPNQIzs4pzIjAzq7iOnxqSdDJwNTAJuC4ilk7wFrOullfMu4GEtUtHawSSJgHfA04B5gKfkzS3k2Uw6yTHvHWDTtcIjgE2RsTTAOlZrmcAT3a4HFZiPXaB2TFvpdfpRHA48GzN+Gayh3rvJmkRsCiNDkl6qkNla4svw3RgR9HlaFXZ10OX7XX2uztUjLFMGPPQHXFf9hhoRrev0zhxP50GY750zUcjYhmwrOhy5EXS6oiYX3Q5WtUr61FW3RD3vRgDPbxO/Y28p9OthrYAs2rGZ6ZpZr3KMW+l1+lE8DAwR9JsSfsCZwF3drgMZp3kmLfS62giiIhh4HzgXmAdcEtEPNHJMhSgFNV9SYOSdkkaSq9Gz0GXYj26TY/FfNfFgKSzJK2TtFPSP0r6yKhFum6d6tDwOiki2lEQKxlJg8CPIuK6osti1gmSTgKuAz4L/AMwAyAifGpuFN9ZXDBJmyRdLOlxSS9LulnSfpIWSnpw1LIh6Yg0fL2k70v6aTrC/6WkwyRdJelFSeslHVXMWpmNr4Mx/9+Bb0TEQxHxp4jY4iQwNieCcjgTOBmYDXwAWNjA+75G1lzsdeBXwCNp/FbgilHLf1vSjrQDDbRebLOmtTXm041884FDJW2UtFnSdyXtn+dK9AonghxJmiXpAUlPSnpC0gVp+jRJ90nakP4ePOqt10TE7yLiBeAnwJF1fuUdEbEmInYBdwC7IuKGiHgDuBmoPTq6BHgPWbv2ZcBPJL13gvWZJOlRSXel8dmSVqUd6+Z08dN6TAtx3Ih2x3wfsA/waeAj6fOPAv5rL8W0pIMk3ZpqQ+skfaiZ7eREkK9hYHFEzAWOA85L3QksAVZGxBxgZRqvta1m+DVgap3f91zN8B/GGN/9ORGxKiJejYjXI2IF8Evg1Ak+/wKyC5wjLgOujIgjgBeBc+ssp3WXZuO4Ee2O+T+kv/8rIrZGxA6y2sIX6a2Yvhr4WUS8D/gg2bo1vJ2cCHKUAu6RNPwq2UY5nKxLgRVpsRXAp+r4uJ3A20dGJB2Wb2kJQOPNlDQTOI3sYhuSBHyUrPoN9a+HdZmc47gRucV8RLxIdhd3bWuYacCB9EhMSzoQ+DfAcoCI+GNEvEQT28mJoE0k9ZNVRVcBfRGxNc3aRlZtncivgfdLOlLSfsDXWyjLQZI+kS7ITZa0gCyAfraXt10FfAX4Uxo/BHgpNYeEbCc7vNkyWXfIIY4bkVvMJz8E/lLSO9Ppkf8J3ETvxPRs4PfAD9PpruskTaGJ7eRE0AaSpgK3ARdGxCu18yJrrzthm92I+C3wDeAXwAbgwb2/Y6/2Ab5FFjQ7gL8EPpW+Y6zynw5sj4g1LXyndbk84rgROcc8wDfJbuj7LfA0sJUs9nvFZOBo4NqIOIqsRrXHaaC6t1NE+JXji+yf7r3ARTXTngJmpOEZwFNFl3OCdfg22dHRJrIjiteAG8mSyOS0zIeAe4suq19ti4Guj+NR69NzMQ0cBmyqGf8IcHcz28k1ghylc47LgXURUdt0807g7DR8NvDjTpetERFxaUTMjKzjqrOA+yNiAfAAWSsM6IL1sOb0ShzX6sWYjohtwLOS/jxNOpGse/OGt5PvLM6RpA8Dfw+s5c3zkF8lO796C/Au4BngzMiazZVeut/g4og4XdJ7yM6xTgMeBT4fEa8XWT7LXy/Gca1eimlJR5Jd/N6X7PTXOWSn/BvaTk4EZmYV51NDZmYVV7oH09SaPn169Pf3F/b9O3fuZMqUKYV9fztVad3WrFmzIyIOLbBIDSk67suol+O1FeP9Lo3GfKkTQX9/P6tXry7s+wcHBxkYGCjs+9upSusm6ZniStO4ouO+jHo5Xlsx3u/SaMz71JCZWcWVukZg5dK/5O6G37Np6WltKIlZb2lm3wK4/uR8Tpc5EVhbOXmYlZ8TQQX1L7mbxfOGWdjkUYiZ9RZfIzAzqzgnAjOzinMiMDOrOCcCM7OKcyIwM6u4lhNBvQ84l/S2NL4xze9v9bvNzKx1edQI6n3A+bnAi2n6lWk5MzMrWEuJoMEHnNc+UPlW4MS0vJmZFajVG8pGHnB+QBrf28OgDweeBYiIYUkvp+V31H6gpEXAIoC+vj4GBwdbLGLzhoaGCv3+dlk8b5i+/bO/ZdTqb96r282sXZpOBLUPOE9P/MlFRCwDlgHMnz8/iuxxsFd7PFyY7iy+fG05byzftGCgpff36nYza5dW/hMcD3xS0qnAfsA7gKuBgyRNTrWCmcCWtPwWYBawWdJk4EDg+Ra+38zMctB0IoiIS4FLYY9ngC6Q9HdkD4O+iT0fnDzyQOVfpfn3h5+TaWY9ptmeRIvUjvsILgEukrSR7BrA8jR9OXBImn4RsKQN321mZg3K5SRxRAwCg2n4aeCYMZbZBXwmj+8zM7P8+M5iM7OKcyIwM6u4crYftLp144WpbiZpE/Aq8AYwHBHzJU0Dbgb6gU3AmRHxYrph8mrgVOA1YGFEPFJEuc32xjUCs8adEBFHRsT8NL4EWBkRc4CVvNkQ4hRgTnotAq7teEnN6uBEYNa62u5TRnerckNkHiK7x2ZGEQU02xufGjJrTAA/lxTAX6c74fsiYmuavw3oS8O7u1VJRrpc2cooZepapYy6qduQTnbdktfv4kRg1pgPR8QWSe8E7pO0vnZmRERKEg0pU9cqZdRN3YYs7OB1u+tPnpLL7+JTQ2YNiIgt6e924A6ye2aeGznlk/5uT4uPdKsyorbLFbPScCIwq5OkKZIOGBkGPg78hje7T4G3dqvyRWWOA16uOYVkVho+NWRWvz7gjvQYjcnA/46In0l6GLhF0rnAM8CZafl7yJqObiRrPnpO54tsNjEnArM6pe5TPjjG9OeBE8eYHsB5HSiaWUucCMzMxlGVGzZ9jcDMrOJcI7DSaeYobNPS09pQErNqaLpGIGmWpAckPSnpCUkXpOnTJN0naUP6e3CaLknXSNoo6XFJR+e1EmZm1rxWTg0NA4sjYi5wHHCepLm43xUzs67SdCKIiK0jPSlGxKvAOrLb593viplZF8nlGoGkfuAoYBUt9rtSpj5XuqF/k2b7Nenbv7N9orRb7Xbqhu1mViYtJwJJU4HbgAsj4pV0sw3QXL8rZepzpRv6N2m2X5PF84a5fG3vtBXYtGBg93A3bDezMmnpP4GkfciSwI0RcXua/JykGRGx1f2uNKYqbZbNrFxaaTUkYDmwLiKuqJnlflfMzLpIKzWC44EvAGslPZamfRVYivtdMTPrGk0ngoh4ENA4s93viplZl+idq4VmZnvha3Djc19DZmYV5xpBm/jow8y6hWsEZmYV50RgZlZxTgRmZhXnRGBmVnG+WGxmXcUNMfLnGoGZWcU5EZiZVZxPDU3A1VAz63WuEZiZVZwTgZlZxfnUkJnlop2nURfPG276aXw2MdcIzMwqruM1AkknA1cDk4DrImJpp7670SOW7OHurjRZa4qMebN6dPS/nKRJwPeAk4DNwMOS7oyIJztZDrNO6daYd2u5aun04e4xwMaIeBpA0k3AGUDDO4UD1bqEY95Kr9OJ4HDg2ZrxzcCxtQtIWgQsSqNDkp7qUNne4sswHdhR1Pe3U6+tmy7bY3T0ur27o4XZ04QxD+WK+zLqtXjNywmXjfu7NBTzpTsBHhHLgGVFlwNA0uqImF90OdrB61YuZYr7MurGbdoJef0unW41tAWYVTM+M00z61WOeSu9TieCh4E5kmZL2hc4C7izw2Uw6yTHvJVeR08NRcSwpPOBe8ma0v0gIp7oZBka1MtVda9bB3RhzJdVabZpyeTyuygi8vgcMzPrUr6z2Mys4pwIzMwqzokAkDRL0gOSnpT0hKQL0vRpku6TtCH9PbjosjZL0iRJj0q6K43PlrRK0kZJN6cLmV1H0kGSbpW0XtI6SR/qpe1WVb0ar61oZ6w7EWSGgcURMRc4DjhP0lxgCbAyIuYAK9N4t7oAWFczfhlwZUQcAbwInFtIqVp3NfCziHgf8EGydeyl7VZVvRqvrWhfrEeEX6NewI/J+oZ5CpiRps0Aniq6bE2uz8wUJB8F7gJEdjfi5DT/Q8C9RZezifU6EPgnUqOHmuk9sd2q+urVeG3xN2lrrLtGMIqkfuAoYBXQFxFb06xtQF9BxWrVVcBXgD+l8WVv3IgAAAFwSURBVEOAlyJiOI1vJusKodvMBn4P/DCdRrhO0hR6Z7tVVa/GayvaGutOBDUkTQVuAy6MiFdq50WWcruura2k04HtEbGm6LK0wWTgaODaiDgK2MmoqnG3breq6vF4bUVbY92JIJG0D1kSuDEibk+Tn5M0I82fAWwvqnwtOB74pKRNwE1k1e2rgYMkjdxQ2K3dHmwGNkfEqjR+K9nO0gvbrap6OV5b0dZYdyIAJAlYDqyLiCtqZt0JnJ2Gzya7dtBVIuLSiJgZEf1k3RvcHxELgAeAT6fFunXdtgHPSvrzNOlEsu6du367VVUvx2sr2h3rvrMYkPRh4O+Btbx5XvKrZNcJbgHeBTwDnBkRLxRSyBxIGgAujojTJb2H7IhrGvAo8PmIeL3I8jVD0pHAdcC+wNPAOWQHOD2z3aqqF+O1Fe2MdScCM7OK86khM7OKcyIwM6s4JwIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OK+//Og4CJNYRCIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrOJS-gZcRXF",
        "outputId": "d8277c30-3093-42a3-b328-5fe549a8cf4f"
      },
      "source": [
        "listaDezenas = []\n",
        "dez1 = numerosSorteados['num1'].tolist()\n",
        "dez2 = numerosSorteados['num2'].tolist()\n",
        "dez3 = numerosSorteados['num3'].tolist()\n",
        "dez4 = numerosSorteados['num4'].tolist()\n",
        "dez5 = numerosSorteados['num5'].tolist()\n",
        "dez6 = numerosSorteados['num6'].tolist()\n",
        "\n",
        "for num in dez1:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "for num in dez2:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "for num in dez3:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "for num in dez4:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "for num in dez5:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "for num in dez6:\n",
        "  listaDezenas.append(num)\n",
        "\n",
        "len(listaDezenas)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14598"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_j4QoHBXkf7",
        "outputId": "8e75bf90-2935-47db-a04d-2e74c9d4d23f"
      },
      "source": [
        "listaDezenas = [x for x in listaDezenas if np.isnan(x) == False]\n",
        "len(listaDezenas)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14592"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzFtPurhXgbK",
        "outputId": "ef0ebe70-aa66-48a7-8e16-d485b2bc31d0"
      },
      "source": [
        "def remove_repetidos(lista):\n",
        "    l = []\n",
        "    for i in lista:\n",
        "        if i not in l:\n",
        "            l.append(i)\n",
        "    l.sort()\n",
        "    return l\n",
        "\n",
        "listaNumeros = remove_repetidos(listaDezenas)\n",
        "\n",
        "\n",
        "print('Todos os',len(listaNumeros), 'já sairam no universo de sorteios')\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Todos os 60 já sairam no universo de sorteios\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4HQKIn_TZrhz",
        "outputId": "d4a0b2f0-cdb0-4054-94fe-99485ae34260"
      },
      "source": [
        "repeticaoN = []\n",
        "repeticao = {}\n",
        "for n in listaNumeros:\n",
        "  repeticaoN = (listaDezenas.count(n))\n",
        "  repeticao[n]= n, repeticaoN\n",
        "  \n",
        "\n",
        "repeticaoDezena = pd.DataFrame.from_dict(repeticao, orient='index')\n",
        "repeticaoDezena.columns = ['dezenas','repetição']\n",
        "\n",
        "dezena = repeticaoDezena['dezenas']\n",
        "repeticao = repeticaoDezena['repetição']\n",
        "\n",
        "repeticao.sort_values(ascending=True).plot(kind='barh', \n",
        "                                           title='Dezenas por ordem de sorteio', \n",
        "                                           figsize=(10,25), \n",
        "                                           fontsize=14, \n",
        "                                           legend=True, \n",
        "                                           color='green',\n",
        "                                           ylabel='Repetição',\n",
        "                                           xlabel='Dezena',                                           \n",
        "                                           )"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb0672a990>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Koovgnlyc3QA",
        "outputId": "c732036b-c0df-4eb8-acc6-24571ea2561e"
      },
      "source": [
        "repeticao.sort_values(ascending=False).head(6).plot(kind='barh', \n",
        "                       title='Numeros mais sorteados', \n",
        "                       figsize=(10,5), \n",
        "                       legend=True, \n",
        "                       color='green',\n",
        "                       xlabel='Dezenas',\n",
        "                       ylabel='Repetição'\n",
        "                       )"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb06445710>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ks4Fr6FxlecHklCdJkqQh4w1nqapHgD8Ezq6qNwC7d68sSZKkDdO4w1mSlwLHAv/RtG3UnZIkSZI2XOMNZ+8CTgOuqKrbk/wO8M3ulSVJkrRh2ng8C1XV9XTOOxua/gnwF90qSpIkaUM1rnCWZHs6j2HaHZgx1F5Vh3SpLkmSpA3SeA9rXgz8GHg+8CFgEZ37j0mSJGkSjfc+Z4NVNS/JrVW1Z9N2c1Xt1/UKx6mvr68GBgZ6XYYkSdIaPdN9zsZ1WBMYutns0iR/ANwHbDsZxUmSJOnXxhvOPpJka+AU4B+ArYB3d60qSZLUc6tXr2bx4sU89thjvS5lvTVjxgzmzJnDJptsMu51xnu15lXN2weBV65DbZIkaT2zePFittxyS+bOnUuSXpez3qkqVqxYweLFi3n+858/7vXW5mrNtwFzh69TVW9ZyzolSdJ64rHHHjOYTUAStttuO5YvX75W6433sOYXgRuArwFPrGVtkiRpPWUwm5h16b/x3kpj86p6f1V9rqouH3qt9d4kSZJa4IILLuC+++57avrEE0/kRz/60ZjLP/744xx++OG86lWv4uSTT+5qbeMdObsqyeFV9eWuViNJklorH5rcUbTqX/PtvJ62fBVVxbOeNd6xpbFdcMEF7LHHHsyaNQuA88477xmX33TTTfnyl6cmBq3NszWvSvJYklVJHkqyqpuFSZIkLVq0iF133ZXjjz+ePfbYgzPOOIP99tuPPffck/7+/qeWedGLXsSxxx7LbrvtxlFHHcUjjzwCwODgIC9/+cuZN28ehx56KEuXLuWyyy5jYGCAY489lr333ptHH32UV7ziFQzdL/Xqq69m3333Za+99uLwww8H4Etf+hIHHHAA++yzD69+9au5//77AVi5ciVHHnkke+65JwceeCC33nrrhD/zuMJZVW1ZVc+qqhlVtVUzvdWE9y5JkrQGd911F29/+9s588wzWbJkCTfddBO33HILg4ODXH9959Hfd955J29/+9u544472GqrrTj77LNZvXo173znO7nssssYHBzkLW95Cx/4wAc46qij6Ovr4+KLL+aWW25hs802e2pfy5cvZ8GCBVxxxRX84Ac/4KKLLgLg4IMP5sYbb+T73/8+Rx99NB//+McB6O/vZ5999uHWW2/lox/9KMcff/yEP+94r9YMcCzw/Ko6I8lOwI5VddOEK5AkSXoGO++8MwceeCDvfe97ueaaa9hnn30AePjhh7nrrrt43vOex0477cRBBx0EwHHHHccnP/lJDjvsMG677TZe85rXAPDEE0+w4447PuO+brzxRl72spex8847A7Dttp177i9evJg3vvGNLF26lMcff/ypW2N861vf4vLLO6fhH3LIIaxYsYJVq1ax1VbrPoY13nPOzgaeBA4BzgAeBs4CWvP4JkmSND1tscUWQOecs9NOO40FCxY8bf6iRYt+46rIJFQVu+++O9/5zncmXMM73/lO3vOe9/D617+e6667joULF054m2MZ7zlnB1TVO4DHAKrqAWDTrlUlSZI0wqGHHsr555/Pww8/DMCSJUtYtmwZAD/96U+fCmGf+cxnOPjgg9l1111Zvnz5U+2rV6/m9ttvB2DLLbfkoYce+o19HHjggdxwww3cc889QOecMoAHH3yQ2bNnA3DhhRc+tfzLXvYyLr74YgCuu+46Zs6cOaFRMxh/OFudZCOg4Kmb0j45oT1LkiSthfnz5/Mnf/InvPSlL+UlL3kJRx111FMBa9ddd+Wss85it91244EHHuDP/uzP2HTTTbnssst4//vfz1577cXee+/Nt7/9bQDe/OY3c/LJJz91QcCQ7bffnnPOOYcjjzyS2bNnP3UO2cKFC3nDG97AvHnzmDlz5lPLL1y4kMHBQfbcc09OPfXUpwW3dZWqNV/GmuRY4I3AvsCFwFHAB6vq8xOuYJL09fXV0FUWkiRp4u644w522223XpexRosWLeJ1r3sdt91226Ru95RTTuH0009n6623ntB2RuvHJINV1Tfa8uO9WvNi4H3Ax4ClwJFtCmaSJEmT6ZhjjuFLX/oSq1evnvJ9j/dqzU8D/1BVZw1rW1hVC7tVmCRJ0njMnTt30kfNLrnkkknd3toY7zlnhwIXJhl+847Xd6EeSZKkDdp4w9ky4PeBNyQ5K8nGgE9ClSRpmhvPueka27r033jDWarqwar6H8By4DpgYmfHSZKkVpsxYwYrVqwwoK2jqmLFihXMmDFjrdYb701orxy2o4VJBoF3r9WeJEnSemXOnDksXryY5cuX97qU9daMGTOYM2fOWq0zrnBWVf1JdgZ2qaqvAV+nM3omSZKmqU022eSpxxRp6oz3as23AScB2wIvAGYD5wCv6l5pa2fwvkHyIU+DkyRpuqv+6X2YdbznnL0DOAhYBVBVdwG/1a2iJEmSNlTjDWe/rKrHhyaaqzWnd2yVJEnqgfGGs/9M8lfAZkleA3we+FL3ypIkSdowjTecnUrnFho/BBYAXwY+2K2iJEmSNlTjvVrzySRfAL5QVV5PK0mS1CXPOHKWjoVJfg7cCdyZZHmS06emPEmSpA3Lmg5rvpvOVZr7VdW2VbUtcABwUBJvQitJkjTJ1hTO3gQcU1X/NdRQVT8BjgOOH3OtNUiyKMkPk9ySZGCU+UnyySR3J7k1yb7rui9JkqT1yZrOOdukqn4+srGqlifZZIL7fuVo2268FtileR0A/FPzU5IkaVpb08jZ4+s4b6KOAP61Om4Enptkxy7uT5IkqRXWNHK2V5JVo7QHWLtHrD9dAdckKeBTVXXuiPmzgXuHTS9u2pZOYJ+SJEmt94zhrKo26tJ+D66qJUl+C7g2yY+r6vq13UiSk+g88xO2nuQKJUmSemC8N6GdVFW1pPm5DLgC2H/EIkuAnYZNz2naRm7n3Krqq6o+Nu9WtZIkSVNnysNZki2SbDn0HpgP3DZisSuB45urNg8EHqwqD2lKkqRpb1xPCJhkOwBXJBna/2eq6uokJwNU1Tl0Hg91OHA38Ajwpz2oU5IkacpNeThr7pO21yjt5wx7X8A7prIuSZKkNujJOWeSJEkaneFMkiSpRQxnkiRJLWI4kyRJahHDmSRJUosYziRJklqkF/c564p5s+Yx0D/Q6zIkSZImxJEzSZKkFjGcSZIktYjhTJIkqUUMZ5IkSS1iOJMkSWoRw5kkSVKLGM4kSZJaxHAmSZLUIoYzSZKkFjGcSZIktYjhTJIkqUUMZ5IkSS1iOJMkSWoRw5kkSVKLGM4kSZJaxHAmSZLUIoYzSZKkFjGcSZIktYjhTJIkqUUMZ5IkSS1iOJMkSWoRw5kkSVKLpKp6XcOkyKwUC3pdhSRJWh9V/9TmoSSDVdU32jxHziRJklrEcCZJktQihjNJkqQWMZxJkiS1iOFMkiSpRQxnkiRJLdK1cJbk/CTLktw2rG3bJNcmuav5uc0Y657QLHNXkhO6VaMkSVLbdHPk7ALgsBFtpwJfr6pdgK8300+TZFugHzgA2B/oHyvESZIkTTddC2dVdT2wckTzEcCFzfsLgSNHWfVQ4NqqWllVDwDX8pshT5IkaVqa6nPOdqiqpc37nwE7jLLMbODeYdOLmzZJkqRpr2cXBFTnuVETelZCkpOSDCQZ4JFJKkySJKmHpjqc3Z9kR4Dm57JRllkC7DRsek7T9huq6tyq6quqPjaf9FolSZKm3FSHsyuBoasvTwC+OMoyXwXmJ9mmuRBgftMmSZI07XXzVhqXAN8Bdk2yOMlbgb8BXpPkLuDVzTRJ+pKcB1BVK4EzgJub14ebNkmSpGkvnVO/1n+ZlWJBr6uQJEnro+qf2jyUZLCq+kab5xMCJEmSWsRwJkmS1CKGM0mSpBYxnEmSJLWI4UySJKlFDGeSJEktsnGvC5gs82bNY6B/oNdlSJIkTYgjZ5IkSS1iOJMkSWoRw5kkSVKLGM4kSZJaxHAmSZLUIoYzSZKkFjGcSZIktYjhTJIkqUUMZ5IkSS1iOJMkSWoRw5kkSVKLGM4kSZJaxHAmSZLUIoYzSZKkFjGcSZIktYjhTJIkqUUMZ5IkSS1iOJMkSWoRw5kkSVKLGM4kSZJaxHAmSZLUIoYzSZKkFklV9bqGSZFZKRb0ugpJkrQ+q/6pyUVJBquqb7R5jpxJkiS1iOFMkiSpRQxnkiRJLWI4kyRJahHDmSRJUosYziRJklqkq+EsyaIkP0xyS5KBpu2MJLc2bdckmTXGuickuat5ndDNOiVJktpiKkbOXllVew+7l8ffVtWeVbU3cBVw+sgVkmwL9AMHAPsD/Um2mYJaJUmSemrKD2tW1aphk1sAo93t7VDg2qpaWVUPANcCh01FfZIkSb20cZe3X8A1SQr4VFWdC5Dkr4HjgQeBV46y3mzg3mHTi5s2SZKkaa3bI2cHV9W+wGuBdyT5fYCq+kBV7QRcDPz5um48yUlJBpIM8MjkFCxJktRLXQ1nVbWk+bkMuILO+WPDXQz80SirLgF2GjY9p2kbuf1zq6qvqvrYfHJqliRJ6qWuhbMkWyTZcug9MB+4LckuwxY7AvjxKKt/FZifZJvmQoD5TZskSdK01s1zznYArkgytJ/PVNXVSS5PsivwJHAPcDJAkj7g5Ko6sapWJjkDuLnZ1oeramUXa5UkSWqFVI12seT6J7NSLOh1FZIkaX1W/VOTi5IMDrvN2NP4hABJkqQWMZxJkiS1iOFMkiSpRQxnkiRJLWI4kyRJahHDmSRJUot0+9maU2berHkM9A/0ugxJkqQJceRMkiSpRQxnkiRJLWI4kyRJahHDmSRJUosYziRJklrEcCZJktQihjNJkqQWMZxJkiS1iOFMkiSpRQxnkiRJLWI4kyRJapFUVa9rmBRJHgLu7HUdG7CZwM97XcQGyr7vLfu/t+z/3rHvJ2bnqtp+tBnT5sHnwJ1V1dfrIjZUSQbs/96w73vL/u8t+7937Pvu8bCmJElSixjOJEmSWmQ6hbNze13ABs7+7x37vrfs/96y/3vHvu+SaXNBgCRJ0nQwnUbOJEmS1nvTIpwlOSzJnUnuTnJqr+uZ7pIsSvLDJLckGWjatk1ybZK7mp/b9LrO6SLJ+UmWJbltWNuo/Z2OTza/C7cm2bd3lU8PY/T/wiRLmt+BW5IcPmzeaU3/35nk0N5UPT0k2SnJN5P8KMntSd7VtPv977Jn6Hu/+1NgvQ9nSTYCzgJeC7wYOCbJi3tb1QbhlVW197DLqE8Fvl5VuwBfb6Y1OS4ADhvRNlZ/vxbYpXmdBPzTFNU4nV3Ab/Y/wJnN78DeVfVlgOZvz9HA7s06Zzd/o7RufgWcUlUvBg4E3tH0sd//7hur78Hvftet9+EM2B+4u6p+UlWPA5cCR/S4pg3REcCFzfsLgSN7WMu0UlXXAytHNI/V30cA/1odNwLPTbLj1FQ6PY3R/2M5Ari0qn5ZVf8F3E3nb5TWQVUtrarvNe8fAu4AZuP3v+ueoe/H4nd/Ek2HcDYbuHfY9GKe+QukiSvgmiSDSU5q2naoqqXN+58BO/SmtA3GWP3t78PU+fPm0Nn5ww7j2/9dkmQusA/wXfz+T6kRfQ9+97tuOoQzTb2Dq2pfOocQ3pHk94fPrM4lwF4GPEXs7574J+AFwN7AUuDvelvO9JbkOcDlwF9W1arh8/z+d9cofe93fwpMh3C2BNhp2PScpk1dUlVLmp/LgCvoDF3fP3T4oPm5rHcVbhDG6m9/H6ZAVd1fVU9U1ZPAP/Prwzf2/yRLsgmdcHBxVf170+z3fwqM1vd+96fGdAhnNwO7JHl+kk3pnJB4ZY9rmraSbJFky6H3wHzgNjp9fkKz2AnAF3tT4QZjrP6+Eji+uWrtQODBYYd/NElGnMf0P+n8DkCn/49O8uwkz6dzYvpNU13fdJEkwKeBO6rq74fN8vvfZWP1vd/9qbHeP/i8qn6V5M+BrwIbAedX1e09Lms62wG4ovN7y8bAZ6rq6iQ3A59L8lbgHuCPe1jjtJLkEuAVwMwki4F+4G8Yvb+/DBxO52TcR4A/nfKCp5kx+v8VSfamczhtEbAAoKpuT/I54Ed0rnZ7R1U90Yu6p4mDgDcBP0xyS9P2V/j9nwpj9f0xfve7zycESJIktch0OKwpSZI0bWop8hcAAAAzSURBVBjOJEmSWsRwJkmS1CKGM0mSpBYxnEmSJLWI4UySJKlFDGeSJEktYjiTJElqkf8PnDm4ynlXHpgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "zQnUMNx0jNUI",
        "outputId": "4fd9b7bd-00fd-4fe3-baa4-41ca20a435e2"
      },
      "source": [
        "repeticao.sort_values(ascending=False).tail(6).plot(kind='barh', \n",
        "                       title='Numeros menos sorteados', \n",
        "                       figsize=(10,5), \n",
        "                       legend=True, \n",
        "                       color='green',\n",
        "                       xlabel='Dezenas',\n",
        "                       ylabel='Repetição'\n",
        "                       )"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb06333e50>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}