Análise_Brasileirão.ipynb

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    "# Uma análise do Campeonato Brasileiro de Futebol\n",
    "## Por Daniele Cavalcante e Leonardo Oliveira\n",
    "Também conhecido como Brasileirão, o Campeonato Brasileiro de Futebol é a principal competição futebolística do país desde 1959. Até hoje, um total de 156 clubes já participaram e apenas 17 conseguiram ser coroados campeões, dos quais 12 conquistaram o título mais de uma vez. \n",
    "\n",
    "Apesar de o campeonato ter início em 1959, o dataset a ser analisado possui dados a partir do ano 2000 até 2021. \n",
    "\n",
    "Nas células a seguir, faremos a importação das bibliotecas necessárias para manipulação de dados e plotagem de histogramas.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "bHGTTcs561Cz"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "LBVaWnWELzo8",
    "outputId": "96f02d2f-1ddd-43b9-8a77-84fe3c2578ee"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Rodada</th>\n",
       "      <th>Data</th>\n",
       "      <th>Horário</th>\n",
       "      <th>Dia</th>\n",
       "      <th>Mandante</th>\n",
       "      <th>Visitante</th>\n",
       "      <th>Vencedor</th>\n",
       "      <th>Arena</th>\n",
       "      <th>Mandante Placar</th>\n",
       "      <th>Visitante Placar</th>\n",
       "      <th>Placar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8342</th>\n",
       "      <td>8343</td>\n",
       "      <td>3</td>\n",
       "      <td>13/06/2021</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>grêmio</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>athletico-pr</td>\n",
       "      <td>Arena do Grêmio</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0x1</td>\n",
       "    </tr>\n",
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       "      <td>8344</td>\n",
       "      <td>3</td>\n",
       "      <td>13/06/2021</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>bahia</td>\n",
       "      <td>internacional</td>\n",
       "      <td>internacional</td>\n",
       "      <td>Pituaçu</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0x1</td>\n",
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       "      <td>8345</td>\n",
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       "      <td>13/06/2021</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>fortaleza</td>\n",
       "      <td>sport</td>\n",
       "      <td>fortaleza</td>\n",
       "      <td>Castelão</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1x0</td>\n",
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       "    <tr>\n",
       "      <th>8345</th>\n",
       "      <td>8346</td>\n",
       "      <td>3</td>\n",
       "      <td>13/06/2021</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>bragantino</td>\n",
       "      <td>fluminense</td>\n",
       "      <td>-</td>\n",
       "      <td>Nabi Abi Chedid</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2x2</td>\n",
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       "    <tr>\n",
       "      <th>8346</th>\n",
       "      <td>8347</td>\n",
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       "      <td>13/06/2021</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>chapecoense</td>\n",
       "      <td>ceará</td>\n",
       "      <td>-</td>\n",
       "      <td>Arena Condá</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0x0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        ID Rodada        Data Horário      Dia     Mandante      Visitante  \\\n",
       "8342  8343      3  13/06/2021   16:00  Domingo       grêmio   athletico-pr   \n",
       "8343  8344      3  13/06/2021   20:30  Domingo        bahia  internacional   \n",
       "8344  8345      3  13/06/2021   20:30  Domingo    fortaleza          sport   \n",
       "8345  8346      3  13/06/2021   20:30  Domingo   bragantino     fluminense   \n",
       "8346  8347      3  13/06/2021   20:30  Domingo  chapecoense          ceará   \n",
       "\n",
       "           Vencedor            Arena Mandante Placar Visitante Placar Placar  \n",
       "8342   athletico-pr  Arena do Grêmio               0                1    0x1  \n",
       "8343  internacional          Pituaçu               0                1    0x1  \n",
       "8344      fortaleza         Castelão               1                0    1x0  \n",
       "8345              -  Nabi Abi Chedid               2                2    2x2  \n",
       "8346              -      Arena Condá               0                0    0x0  "
      ]
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   ],
   "source": [
    "# Carrega o dataset\n",
    "dfDataChampionship = pd.read_csv(\"./campeonato-brasileiro-full.csv\", delimiter=\";\")\n",
    "\n",
    "# String\n",
    "dfDataChampionship = dfDataChampionship.applymap(str);  \n",
    "\n",
    "# Cria uma nova coluna 'Placar'\n",
    "dfDataChampionship[\"Placar\"] = dfDataChampionship[\"Mandante Placar\"].map(str) + \"x\" + dfDataChampionship[\"Visitante Placar\"]\n",
    "\n",
    "# Remove colunas desnecessárias \n",
    "dfDataChampionship = dfDataChampionship.drop(['Estado Mandante', 'Estado Visitante', 'Estado Vencedor'], axis=1)\n",
    "\n",
    "# Exibe as primeiras linhas da tabela\n",
    "dfDataChampionship.tail()"
   ]
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    "outputId": "1339de16-e350-4a36-97d9-be89a0c8df68"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2000-07-29\n",
       "1      2000-07-29\n",
       "2      2000-07-29\n",
       "3      2000-07-30\n",
       "4      2000-07-30\n",
       "          ...    \n",
       "8342   2021-06-13\n",
       "8343   2021-06-13\n",
       "8344   2021-06-13\n",
       "8345   2021-06-13\n",
       "8346   2021-06-13\n",
       "Name: Data, Length: 8347, dtype: datetime64[ns]"
      ]
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    "#dfDataChampionship['Data'].unique()\n",
    "\n",
    "# Converte a coluna 'Data' no formato %d/%m/%y para o formato %y-%m-%d\n",
    "pd.to_datetime(dfDataChampionship['Data'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hVx5BE2LQMLa"
   },
   "source": [
    "# Campeonato Brasileirão 2020\n",
    "\n",
    "Existem 20 clubes no Brasileirão. Durante uma temporada (de maio a dezembro), cada clube joga os outros duas vezes (um sistema duplo de jogos), uma vez em seu estádio e outra em seus adversários, por um total de 38 jogos. As equipes recebem três pontos por vitória e um ponto por empate. Nenhum ponto é concedido por uma perda. As equipes são classificadas por total de pontos, vitórias, diferença de gols e gols marcados. No final de cada temporada, o clube com mais pontos é coroado campeão. \n",
    "\n",
    "\n",
    "Sabendo disso, nas próximas células, faremos a filtragem de partidas ocorridas entre  maio de 2020 até fevereiro de 2021 (término atrasado devido a pandemia do Coronavírus)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zGeosmJeNiFS"
   },
   "outputs": [],
   "source": [
    "# PG: pontos ganhos j: jogos realizados V: vitórias E: empates D: derrotas\n",
    "# GP: gols pró(gols feitos) GC: gols contra(gols sofridos) SG: saldo de gols(GP - GC)\n",
    "d = {'Clubes': [], 'PG': [], 'j': [], 'V': [], 'E': [], 'D': [], 'GP': [], 'GC': [], 'SG': []}\n",
    "dfClassChampionship = pd.DataFrame(data=d);"
   ]
  },
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   "execution_count": 5,
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Rodada</th>\n",
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       "      <th>7939</th>\n",
       "      <td>7940</td>\n",
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       "      <td>2020-08-08</td>\n",
       "      <td>19:00</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>Athlético-PR</td>\n",
       "      <td>Athlético-PR</td>\n",
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       "      <td>19:30</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>Coritiba</td>\n",
       "      <td>Internacional</td>\n",
       "      <td>Internacional</td>\n",
       "      <td>Couto Pereira</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>7941</th>\n",
       "      <td>7942</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>21:00</td>\n",
       "      <td>Sábado</td>\n",
       "      <td>Sport</td>\n",
       "      <td>Ceará</td>\n",
       "      <td>Sport</td>\n",
       "      <td>Ilha do Retiro</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3x2</td>\n",
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       "    <tr>\n",
       "      <th>7942</th>\n",
       "      <td>7943</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-09</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>Flamengo</td>\n",
       "      <td>Atlético-MG</td>\n",
       "      <td>Atlético-MG</td>\n",
       "      <td>Maracanã</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0x1</td>\n",
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       "      <th>7943</th>\n",
       "      <td>7944</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-09</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Domingo</td>\n",
       "      <td>Santos</td>\n",
       "      <td>Bragantino</td>\n",
       "      <td>-</td>\n",
       "      <td>Vila Belmiro</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1x1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8314</th>\n",
       "      <td>8315</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>21:30</td>\n",
       "      <td>Quinta-Feira</td>\n",
       "      <td>Internacional</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>-</td>\n",
       "      <td>Beira Rio</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0x0</td>\n",
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       "    <tr>\n",
       "      <th>8315</th>\n",
       "      <td>8316</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>21:30</td>\n",
       "      <td>Quinta-Feira</td>\n",
       "      <td>Bahia</td>\n",
       "      <td>Santos</td>\n",
       "      <td>Bahia</td>\n",
       "      <td>Itaipava Arena Fonte Nova</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2x0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8316</th>\n",
       "      <td>8317</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>21:30</td>\n",
       "      <td>Quinta-Feira</td>\n",
       "      <td>Athlético-PR</td>\n",
       "      <td>Sport</td>\n",
       "      <td>Athlético-PR</td>\n",
       "      <td>Arena da Baixada</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2x0</td>\n",
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       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <td>8318</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>21:30</td>\n",
       "      <td>Quinta-Feira</td>\n",
       "      <td>Ceará</td>\n",
       "      <td>Botafogo-RJ</td>\n",
       "      <td>Ceará</td>\n",
       "      <td>Castelão</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2x1</td>\n",
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       "    <tr>\n",
       "      <th>8318</th>\n",
       "      <td>8319</td>\n",
       "      <td>38</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>21:30</td>\n",
       "      <td>Quinta-Feira</td>\n",
       "      <td>Atlético-GO</td>\n",
       "      <td>Coritiba</td>\n",
       "      <td>Atlético-GO</td>\n",
       "      <td>Antônio Accioly</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3x1</td>\n",
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       "<p>380 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        ID Rodada       Data Horário           Dia       Mandante  \\\n",
       "7939  7940      1 2020-08-08   19:00        Sábado      Fortaleza   \n",
       "7940  7941      1 2020-08-08   19:30        Sábado       Coritiba   \n",
       "7941  7942      1 2020-08-08   21:00        Sábado          Sport   \n",
       "7942  7943      1 2020-08-09   16:00       Domingo       Flamengo   \n",
       "7943  7944      1 2020-08-09   16:00       Domingo         Santos   \n",
       "...    ...    ...        ...     ...           ...            ...   \n",
       "8314  8315     38 2021-02-25   21:30  Quinta-Feira  Internacional   \n",
       "8315  8316     38 2021-02-25   21:30  Quinta-Feira          Bahia   \n",
       "8316  8317     38 2021-02-25   21:30  Quinta-Feira   Athlético-PR   \n",
       "8317  8318     38 2021-02-25   21:30  Quinta-Feira          Ceará   \n",
       "8318  8319     38 2021-02-25   21:30  Quinta-Feira    Atlético-GO   \n",
       "\n",
       "          Visitante       Vencedor                      Arena Mandante Placar  \\\n",
       "7939   Athlético-PR   Athlético-PR                   Castelão               0   \n",
       "7940  Internacional  Internacional              Couto Pereira               0   \n",
       "7941          Ceará          Sport             Ilha do Retiro               3   \n",
       "7942    Atlético-MG    Atlético-MG                   Maracanã               0   \n",
       "7943     Bragantino              -               Vila Belmiro               1   \n",
       "...             ...            ...                        ...             ...   \n",
       "8314    Corinthians              -                  Beira Rio               0   \n",
       "8315         Santos          Bahia  Itaipava Arena Fonte Nova               2   \n",
       "8316          Sport   Athlético-PR           Arena da Baixada               2   \n",
       "8317    Botafogo-RJ          Ceará                   Castelão               2   \n",
       "8318       Coritiba    Atlético-GO            Antônio Accioly               3   \n",
       "\n",
       "     Visitante Placar Placar  \n",
       "7939                2    0x2  \n",
       "7940                1    0x1  \n",
       "7941                2    3x2  \n",
       "7942                1    0x1  \n",
       "7943                1    1x1  \n",
       "...               ...    ...  \n",
       "8314                0    0x0  \n",
       "8315                0    2x0  \n",
       "8316                0    2x0  \n",
       "8317                1    2x1  \n",
       "8318                1    3x1  \n",
       "\n",
       "[380 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDataChampionship['Data'] = pd.to_datetime(dfDataChampionship['Data'], infer_datetime_format=True)\n",
    "\n",
    "filter1  =  dfDataChampionship[\"Data\"]>='2020-01-01'\n",
    "filter2  =  dfDataChampionship[\"Data\"]<='2021-02-26' # o Brasileirao 2020 teve seu fim em 2021 devido a pandemia\n",
    "\n",
    "brasilianLeague = dfDataChampionship[filter1 & filter2]\n",
    "brasilianLeague"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3_-Ft4xiRRFT"
   },
   "source": [
    "Feito a filtragem de todas as partidas ocorridas no Brasileirão 2020, iremos retornar um novo DataFrame contendo todos os nomes de clubes participantes ao concatenar as colunas 'Mandante' e 'Visitante' sem repetições."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "Y-2JwIX3OYFY",
    "outputId": "6249c40a-8875-49e7-cc2d-80056e7db6a3"
   },
   "outputs": [
    {
     "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>Clubes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fortaleza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>coritiba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flamengo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>santos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>grêmio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>botafogo-rj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>corinthians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>goiás</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>palmeiras</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bragantino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>atlético-mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>athlético-pr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bahia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>atlético-go</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fluminense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ceará</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>são paulo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>internacional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>vasco</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Clubes\n",
       "0       fortaleza\n",
       "1        coritiba\n",
       "2           sport\n",
       "3        flamengo\n",
       "4          santos\n",
       "5          grêmio\n",
       "6     botafogo-rj\n",
       "7     corinthians\n",
       "8           goiás\n",
       "9       palmeiras\n",
       "10     bragantino\n",
       "11    atlético-mg\n",
       "12   athlético-pr\n",
       "13          bahia\n",
       "14    atlético-go\n",
       "15     fluminense\n",
       "16          ceará\n",
       "17      são paulo\n",
       "18  internacional\n",
       "19          vasco"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cancatenando as colunas\n",
    "clubs  =  pd.concat([brasilianLeague['Mandante'].str.lower(), brasilianLeague['Visitante'].str.lower()], axis=1, keys=['Clubes'])\n",
    "cb  =  pd.Series(clubs['Clubes'].unique(), name=\"Clubes\") # unique retorna valores sem repetição\n",
    "cb  =  cb.to_frame() # convert Series to DataFrame \n",
    "cb # exibe o dataframe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2PZQgjs9TQ8y"
   },
   "source": [
    "Obtendo-se todos os clubes participantes, a próxima célula contém funções que irá calcular, para cada time, o número de vitórias, partidas, pontos, empate, derrota, gols feitos e gols sofridos. \n",
    "Passaremos como paramêtro o período ocorrido e o clube.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "AwOq_SpKOnHd"
   },
   "outputs": [],
   "source": [
    "# Retorna o numero de vitórias do time\n",
    "def getNumberWinner(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter = df[\"Vencedor\"].str.lower() == clube\n",
    "    return (data[filter]['Vencedor'].count()).astype(np.int64)\n",
    "\n",
    "# Retorna o número de partidas realizadas pelo time\n",
    "def getNumberDepartures(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter1 = df[\"Mandante\"].str.lower() == clube\n",
    "    filter2 = df[\"Visitante\"].str.lower() == clube\n",
    "    return (data[filter1]['Mandante'].count() + data[filter2]['Visitante'].count()).astype(np.int64)\n",
    "\n",
    "# Retorna o número de pontos conquistados pelo time\n",
    "def getPoints(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter1 = df[\"Mandante\"].astype(str).str.lower() == clube\n",
    "    filter2 = df[\"Visitante\"].astype(str).str.lower() == clube\n",
    "    filter3 = df[\"Vencedor\"].astype(str).str.lower() == clube\n",
    "    filter4 = (df[\"Mandante\"].astype(str).str.lower() == clube) | (df[\"Visitante\"].astype(str).str.lower() == clube)\n",
    "    filter5 = df[\"Vencedor\"].astype(str).str.lower() == '-'\n",
    "    \n",
    "    v1 = data[(filter1) & (filter3)]\n",
    "    v1 = v1['Vencedor'].count()\n",
    "    v2 = data[(filter2) & (filter3)]\n",
    "    v2 = v2['Vencedor'].count() \n",
    "    v3 = data[(filter4) & (filter5)]\n",
    "    v3 = v3['Vencedor'].count()\n",
    "    return ((v2*3)+(v1*3)+v3).astype(np.int64)\n",
    "\n",
    "# Retorna o número de empates do time\n",
    "def getDraw(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter1 = (df[\"Mandante\"].astype(str).str.lower() == clube) | (df[\"Visitante\"].astype(str).str.lower() == clube)\n",
    "    filter2 = df[\"Vencedor\"].astype(str).str.lower() == '-'\n",
    "    df = data[(filter1) & (filter2)]\n",
    "    empates = df['Vencedor'].astype(str).count()\n",
    "    return empates.astype(np.int64)\n",
    "\n",
    "# Retorna o número de derrotas do time\n",
    "def getDefeats(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter1 = (df[\"Mandante\"].astype(str).str.lower() == clube) | (df[\"Visitante\"].astype(str).str.lower() == clube)\n",
    "    filter2 = (df[\"Vencedor\"].astype(str).str.lower() != clube) & (df[\"Vencedor\"].astype(str).str.lower() != '-')\n",
    "    df = data[(filter1) & (filter2)]\n",
    "    derrotas = df['Vencedor'].count()\n",
    "    return derrotas.astype(np.int64)\n",
    "\n",
    "# Retorna os gols feitos e gols tomados pelo time\n",
    "def getGP(data, clube ):\n",
    "    df = data.apply(lambda x: x.astype(str).str.strip())\n",
    "    filter1  =  df[\"Mandante\"].astype(str).str.lower() == clube\n",
    "    filter2  =  df[\"Visitante\"].astype(str).str.lower() == clube\n",
    "    df1      =  data[(filter1)]\n",
    "    df2      =  data[(filter2)]\n",
    "    placar1  =  df1['Placar'].astype(str).str.split('x')\n",
    "    placar2  =  df2['Placar'].astype(str).str.split('x')\n",
    "    \n",
    "    gp       =  0\n",
    "    gc       =  0\n",
    "    for g1, g2 in placar1:\n",
    "        gp = (gp + pd.to_numeric( g1 ))\n",
    "        gc = (gc + pd.to_numeric( g2 ))\n",
    "            \n",
    "    for g1, g2 in placar2:\n",
    "        gp = (gp + pd.to_numeric( g2 )) \n",
    "        gc = (gc + pd.to_numeric( g1 ))\n",
    "    return gp, gc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkskBzemTxOp"
   },
   "source": [
    "Com as funções definidas, agora podemos montar a tabela geral para o Brasileirão 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "57k_oDWzPHwc",
    "outputId": "d930ac97-07dd-404c-a2ac-c89f1ea94c64"
   },
   "outputs": [
    {
     "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>Clubes</th>\n",
       "      <th>PG</th>\n",
       "      <th>J</th>\n",
       "      <th>V</th>\n",
       "      <th>E</th>\n",
       "      <th>D</th>\n",
       "      <th>GP</th>\n",
       "      <th>GC</th>\n",
       "      <th>SG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flamengo</td>\n",
       "      <td>71</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Internacional</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>35</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlético-mg</td>\n",
       "      <td>68</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>São paulo</td>\n",
       "      <td>66</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fluminense</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grêmio</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>58</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Santos</td>\n",
       "      <td>54</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bragantino</td>\n",
       "      <td>53</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Athlético-pr</td>\n",
       "      <td>53</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Corinthians</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Atlético-go</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>48</td>\n",
       "      <td>59</td>\n",
       "      <td>-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sport</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Vasco</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Goiás</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "      <td>63</td>\n",
       "      <td>-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Coritiba</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Botafogo-rj</td>\n",
       "      <td>27</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>62</td>\n",
       "      <td>-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Clubes  PG   J   V   E   D  GP  GC  SG\n",
       "0        Flamengo  71  38  21   8   9  68  48  20\n",
       "1   Internacional  70  38  20  10   8  61  35  26\n",
       "2     Atlético-mg  68  38  20   8  10  64  45  19\n",
       "3       São paulo  66  38  18  12   8  59  41  18\n",
       "4      Fluminense  64  38  18  10  10  55  42  13\n",
       "5          Grêmio  59  38  14  17   7  53  40  13\n",
       "6       Palmeiras  58  38  15  13  10  51  37  14\n",
       "7          Santos  54  38  14  12  12  52  51   1\n",
       "8      Bragantino  53  38  13  14  11  50  40  10\n",
       "9    Athlético-pr  53  38  15   8  15  38  36   2\n",
       "10          Ceará  52  38  14  10  14  54  51   3\n",
       "11    Corinthians  51  38  13  12  13  45  45   0\n",
       "12    Atlético-go  50  38  12  14  12  40  45  -5\n",
       "13          Bahia  44  38  12   8  18  48  59 -11\n",
       "14          Sport  42  38  12   6  20  31  50 -19\n",
       "15      Fortaleza  41  38  10  11  17  34  44 -10\n",
       "16          Vasco  41  38  10  11  17  37  56 -19\n",
       "17          Goiás  37  38   9  10  19  41  63 -22\n",
       "18       Coritiba  31  38   7  10  21  31  54 -23\n",
       "19    Botafogo-rj  27  38   5  12  21  32  62 -30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTable = cb[[\"Clubes\"]].copy()\n",
    "for column in [\"PG\", \"J\", \"V\", \"E\", \"D\", \"GP\", \"GC\", \"SG\"]:\n",
    "    dfTable[column] = 0\n",
    "\n",
    "def ensureUtf(s, encoding='utf8'):\n",
    "  if type(s) == bytes:\n",
    "    return s.decode(encoding, 'ignore')\n",
    "  else:\n",
    "    return s\n",
    "\n",
    "for index, row in dfTable.iterrows():\n",
    "    c1  =  row['Clubes']\n",
    "    c1  =  ensureUtf( c1 )\n",
    "    c1  =  c1.strip()\n",
    "    \n",
    "    pg       =  getPoints(brasilianLeague, c1 )\n",
    "    j        =  getNumberDepartures(brasilianLeague, c1 )\n",
    "    v        =  getNumberWinner(brasilianLeague, c1 )\n",
    "    e        =  getDraw(brasilianLeague, c1 )\n",
    "    d        =  getDefeats(brasilianLeague, c1 )\n",
    "    gp, gc   =  getGP(brasilianLeague, c1 )\n",
    "    \n",
    "    dfTable.at[index, 'PG'] = pg\n",
    "    dfTable.at[index, 'J']  = j\n",
    "    dfTable.at[index, 'V']  = v\n",
    "    dfTable.at[index, 'E']  = e\n",
    "    dfTable.at[index, 'D']  = d\n",
    "    dfTable.at[index, 'GP'] = gp\n",
    "    dfTable.at[index, 'GC'] = gc\n",
    "    dfTable.at[index, 'SG'] = gp - gc\n",
    "\n",
    "dfTable['Clubes'] = dfTable['Clubes'].str.capitalize() \n",
    "dfTable  =  dfTable.sort_values(by=['PG'], ascending=False) # ordena os valores\n",
    "dfTable  =  dfTable.reset_index(drop=True)\n",
    "\n",
    "dfTable # exibe a tabela do Brasileirao 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aWkKtcH9aD_Z"
   },
   "source": [
    "Agora que temos o DataFrame do Brasileirão 2020 completo, as próximas células farão uso desses dados para gerar gráficos de comparação entre os times de fácil visualização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 538
    },
    "id": "l0nP3rtqukeZ",
    "outputId": "a0f64605-755d-4ac4-991d-7d49a2f92ca7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de pontos ganhos por time no Brasileirão janeiro/2020 - fevereiro/2021\n",
    "plt.figure(figsize=(20, 10))\n",
    "pg = dfTable['PG'].sort_values(ascending=False)\n",
    "pg.sort_values(ascending=True, inplace=True)\n",
    "\n",
    "plt.barh(dfTable['Clubes'], dfTable['PG'], label='Pontos')\n",
    "\n",
    "plt.title('Pontuação geral do Brasileirão 2020', size=20)\n",
    "plt.xlabel(\"Pontos\", size=20)\n",
    "plt.ylabel(\"Times\", size=20)\n",
    "\n",
    "for index, value in enumerate(dfTable['PG']):\n",
    "    plt.text(value, index, str(value)) \n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cHdFPIVUbdrV"
   },
   "source": [
    "Por ser o líder no ranking de pontuação, concluimos que o Flamento foi o campeão do Brasileirão 2020.\n",
    "\n",
    "Além disso, desde a edição de 2016, os seis principais clubes do Brasileirão se classificam para a Copa Libertadores do próximo ano, que são eles: Flamengo, Internacional, Atlético-MG, São Paulo, Fluminense e Grêmio, segundo o gráfico acima. \n",
    "\n",
    "Os quatro principais clubes - Flamengo, Internacional, Atlético-MG e São Paulo - entram diretamente na fase de grupos. O quinto e o sexto colocados - Fluminense e Grêmio -  entram na Libertadores na segunda rodada e devem vencer duas etapas eliminatórias para entrar na fase de grupos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "wRdexoS2dnGZ",
    "outputId": "84aff155-c35c-4444-a8ff-fe01804f8c54"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2376x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(33, 15))\n",
    "\n",
    "Pos = np.array(range(len(dfTable)))\n",
    "width = 0.2\n",
    "\n",
    "plt.bar(Pos-width, dfTable['V'], width=width, label='Vitórias')\n",
    "plt.bar(Pos, dfTable['E'], width=width, label='Empates')\n",
    "plt.bar(Pos+width, dfTable['D'], width=width, label='Derrotas')\n",
    "\n",
    "plt.title('Comparação de vitória, empate e derrota para cada time - Brasileirão 2020', size=20)\n",
    "plt.xlabel(\"Times\", size=20)\n",
    "plt.ylabel(\"Número de partidas\", size=20)\n",
    "\n",
    "plt.xticks(Pos, dfTable['Clubes'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2oc7Wz28JsK0"
   },
   "source": [
    "Sabemos que a quantidade de partidas jogadas para cada time é a mesma (38 partidas), então a soma de vitória, derrota e empate para cada time é o mesmo. \n",
    "\n",
    "Além disso, percebemos que o número de vitórias do primeiro time (Flamengo) é aproximadamento igual ao número de derrotas do último time (Botafogo-rj), o mesmo vale para o segundo e penúltimo time, e assim suscessivamente. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "SdkRvqp2moti",
    "outputId": "1bb94ce6-ba02-4443-c1fd-bf0c1a86d365"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "\n",
    "Pos = np.array(range(len(dfTable)))\n",
    "width = 0.2\n",
    "\n",
    "plt.bar(Pos-width, dfTable['GP'], width=width, label='Gols feitos')\n",
    "plt.bar(Pos, dfTable['GC'], width=width, color='red', label='Gols sofridos')\n",
    "plt.bar(Pos+width, dfTable['SG'], width=width, color='green', label='Saldo geral')\n",
    "\n",
    "plt.title('Comparação entre gols feitos, sofridos e saldo geral de gols - Brasileirão 2020', size=20)\n",
    "plt.xlabel(\"Times\", size=20)\n",
    "plt.ylabel(\"Gols\", size=20)\n",
    "\n",
    "plt.xticks(Pos, dfTable['Clubes'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j4dwiKlTdpY5"
   },
   "source": [
    "Para as próximas análises, ainda sobre o Brasileirão 2020, utilizaremos um segundo dataset correlacionado com os dados do primeiro. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "id": "IrBtpoG8ef7Y",
    "outputId": "27e9e439-141c-4405-9bbb-013dc72a4c9c"
   },
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>Mandante</th>\n",
       "      <th>Posse de bola</th>\n",
       "      <th>Passes</th>\n",
       "      <th>Precisão de passe</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Cartões amarelos</th>\n",
       "      <th>Cartões vermelhos</th>\n",
       "      <th>Impedimentos</th>\n",
       "      <th>Escanteios</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Chutes(Chutes a gol)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6041</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>13.0(0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6041</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.0(0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6040</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>17.0(0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6040</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.0(0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6042</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>26.0(11.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4621</th>\n",
       "      <td>8345</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>5.0(0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4622</th>\n",
       "      <td>8346</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.0(5.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4623</th>\n",
       "      <td>8346</td>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.0(4.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4624</th>\n",
       "      <td>8347</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.0(3.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4625</th>\n",
       "      <td>8347</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>14.0(6.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4626 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Mandante Posse de bola Passes Precisão de passe Faltas  \\\n",
       "0     6041        1          45.0  326.0               nan   20.0   \n",
       "1     6041        0          55.0  388.0               nan   21.0   \n",
       "2     6040        1          50.0  406.0               nan   20.0   \n",
       "3     6040        0          50.0  426.0               nan   14.0   \n",
       "4     6042        1          74.0  702.0               nan    9.0   \n",
       "...    ...      ...           ...    ...               ...    ...   \n",
       "4621  8345        0          41.0  334.0              75.0   16.0   \n",
       "4622  8346        1          47.0  390.0              78.0   11.0   \n",
       "4623  8346        0          53.0  437.0              77.0    8.0   \n",
       "4624  8347        1          38.0  307.0              76.0   16.0   \n",
       "4625  8347        0          62.0  507.0              84.0   14.0   \n",
       "\n",
       "     Cartões amarelos Cartões vermelhos Impedimentos Escanteios Unnamed: 12  \\\n",
       "0                 2.0               0.0          3.0        3.0         nan   \n",
       "1                 2.0               0.0          0.0        5.0         nan   \n",
       "2                 2.0               0.0          2.0       11.0         nan   \n",
       "3                 4.0               0.0          4.0        2.0         nan   \n",
       "4                 2.0               0.0          1.0        5.0         nan   \n",
       "...               ...               ...          ...        ...         ...   \n",
       "4621              3.0               1.0          0.0        3.0         nan   \n",
       "4622              3.0               0.0          0.0       10.0         nan   \n",
       "4623              1.0               0.0          2.0       10.0         nan   \n",
       "4624              2.0               0.0          0.0        2.0         nan   \n",
       "4625              2.0               0.0          1.0        7.0         nan   \n",
       "\n",
       "     Unnamed: 13 Unnamed: 14 Unnamed: 15 Chutes(Chutes a gol)  \n",
       "0            nan         nan         nan            13.0(0.0)  \n",
       "1            nan         nan         nan            16.0(0.0)  \n",
       "2            nan         nan         nan            17.0(0.0)  \n",
       "3            nan         nan         nan            10.0(0.0)  \n",
       "4            nan         nan         nan           26.0(11.0)  \n",
       "...          ...         ...         ...                  ...  \n",
       "4621         nan         nan         nan             5.0(0.0)  \n",
       "4622         nan         nan         nan            16.0(5.0)  \n",
       "4623         nan         nan         nan            15.0(4.0)  \n",
       "4624         nan         nan         nan             8.0(3.0)  \n",
       "4625         nan         nan         nan            14.0(6.0)  \n",
       "\n",
       "[4626 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carrega o dataset\n",
    "dfDataStatistic = pd.read_csv(\"./campeonato-brasileiro-estatisticas-full.csv\", delimiter=\";\")\n",
    "\n",
    "# String\n",
    "dfDataStatistic = dfDataStatistic.applymap(str);\n",
    "  \n",
    "# Cria uma nova coluna \"Finalizações(Chutes a gol)\"\n",
    "dfDataStatistic[\"Chutes(Chutes a gol)\"] = dfDataStatistic[\"Chutes\"].map(str) + \"(\" + dfDataStatistic[\"Chutes a gol\"] + \")\"\n",
    "\n",
    "# Remove colunas desnecessárias \n",
    "dfDataStatistic = dfDataStatistic.drop(['Chutes', 'Chutes a gol'], axis=1)\n",
    "\n",
    "# Exibe parte da tabela\n",
    "dfDataStatistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "1V_s0WwGzlNn",
    "outputId": "88f31aa0-2f86-4c00-8324-69b65fcdddeb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>Clube</th>\n",
       "      <th>Posse de bola</th>\n",
       "      <th>Passes</th>\n",
       "      <th>Precisão de passe</th>\n",
       "      <th>Faltas</th>\n",
       "      <th>Cartões amarelos</th>\n",
       "      <th>Cartões vermelhos</th>\n",
       "      <th>Impedimentos</th>\n",
       "      <th>Escanteios</th>\n",
       "      <th>Chutes(Chutes a gol)</th>\n",
       "      <th>Rodada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7940</td>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>43.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0(3.0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7940</td>\n",
       "      <td>Athlético-PR</td>\n",
       "      <td>57.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0(5.0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7941</td>\n",
       "      <td>Coritiba</td>\n",
       "      <td>42.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0(0.0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7941</td>\n",
       "      <td>Internacional</td>\n",
       "      <td>58.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0(4.0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7942</td>\n",
       "      <td>Sport</td>\n",
       "      <td>40.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0(3.0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>767</td>\n",
       "      <td>8314</td>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>48.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0(4.0)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>768</td>\n",
       "      <td>8311</td>\n",
       "      <td>Vasco</td>\n",
       "      <td>48.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0(12.0)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>769</td>\n",
       "      <td>8311</td>\n",
       "      <td>Goiás</td>\n",
       "      <td>52.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0(5.0)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>770</td>\n",
       "      <td>8310</td>\n",
       "      <td>Fluminense</td>\n",
       "      <td>48.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0(3.0)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>771</td>\n",
       "      <td>8310</td>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>52.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0(2.0)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>760 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index    ID          Clube Posse de bola Passes Precisão de passe Faltas  \\\n",
       "0        0  7940      Fortaleza          43.0  414.0              86.0   14.0   \n",
       "1        1  7940   Athlético-PR          57.0  549.0              89.0   10.0   \n",
       "2        2  7941       Coritiba          42.0  382.0              74.0   11.0   \n",
       "3        3  7941  Internacional          58.0  504.0              81.0   21.0   \n",
       "4        4  7942          Sport          40.0  324.0              77.0   15.0   \n",
       "..     ...   ...            ...           ...    ...               ...    ...   \n",
       "755    767  8314      Palmeiras          48.0  424.0              80.0   17.0   \n",
       "756    768  8311          Vasco          48.0  390.0              85.0   12.0   \n",
       "757    769  8311          Goiás          52.0  405.0              84.0   18.0   \n",
       "758    770  8310     Fluminense          48.0  401.0              86.0   10.0   \n",
       "759    771  8310      Fortaleza          52.0  422.0              85.0   12.0   \n",
       "\n",
       "    Cartões amarelos Cartões vermelhos Impedimentos Escanteios  \\\n",
       "0                3.0               1.0          1.0        4.0   \n",
       "1                2.0               0.0          1.0        4.0   \n",
       "2                3.0               0.0          3.0        2.0   \n",
       "3                4.0               0.0          2.0        6.0   \n",
       "4                3.0               0.0          3.0        2.0   \n",
       "..               ...               ...          ...        ...   \n",
       "755              0.0               0.0          1.0        4.0   \n",
       "756              1.0               0.0          2.0        9.0   \n",
       "757              2.0               0.0          2.0        6.0   \n",
       "758              0.0               0.0          0.0        3.0   \n",
       "759              0.0               0.0          1.0        5.0   \n",
       "\n",
       "    Chutes(Chutes a gol) Rodada  \n",
       "0              11.0(3.0)      1  \n",
       "1              13.0(5.0)      1  \n",
       "2               2.0(0.0)      1  \n",
       "3              11.0(4.0)      1  \n",
       "4               9.0(3.0)      1  \n",
       "..                   ...    ...  \n",
       "755             9.0(4.0)     38  \n",
       "756           27.0(12.0)     38  \n",
       "757            20.0(5.0)     38  \n",
       "758             9.0(3.0)     38  \n",
       "759             9.0(2.0)     38  \n",
       "\n",
       "[760 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Essa célula tem como função extrair do dfDataStatistic apenas as linhas que tem relaçao com o dataframe do Brasileirao 2020 extraido anteriormente\n",
    "# Para isso, faremos o inner join dos dois dataframes\n",
    "\n",
    "m = pd.merge(dfDataStatistic, brasilianLeague, how = 'inner', on = 'ID')\n",
    "\n",
    "# Cria uma nova coluna e preenche com valores da coluna Mandante_y ou Visitante \n",
    "m[\"Clube\"] = \"\"\n",
    "m.loc[m['Mandante_x'] == '1', 'Clube'] = m['Mandante_y']\n",
    "m.loc[m['Mandante_x'] == '0', 'Clube'] = m['Visitante']\n",
    "\n",
    "# Dropa colunas desnecessárias\n",
    "m = m.drop(['Data', 'Horário', 'Dia', 'Mandante_y', 'Visitante', 'Vencedor', 'Arena', 'Mandante Placar', \n",
    "            'Visitante Placar', 'Placar', 'Mandante_x', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15'], axis=1)\n",
    "\n",
    "# Altera a posição da coluna\n",
    "first_column = m.pop('Clube')\n",
    "m.insert(1, 'Clube', first_column)\n",
    "\n",
    "# Remove linhas duplicadas\n",
    "m.drop_duplicates(inplace=True)\n",
    "\n",
    "m = m.reset_index()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qyRp360DyLDt"
   },
   "source": [
    "A próxima célula verifica se há uma tendência para a relação entre a precisão de passe e posse de bola."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "id": "ZNrpYouFjYEa",
    "outputId": "0d179615-d618-4640-e5a7-648610829060"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(9.924999999999997, 0.5, 'Precisão de passe em %')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m['Posse de bola']= pd.to_numeric(m['Posse de bola'], errors='coerce')\n",
    "m['Precisão de passe']= pd.to_numeric(m['Precisão de passe'], errors='coerce')\n",
    "\n",
    "sn.lmplot(x='Posse de bola', y='Precisão de passe', data=m, height=10)\n",
    "\n",
    "plt.title('Distribuição: Precisão de passe x Posse de bola')\n",
    "plt.xlabel('Posse de bola em %')\n",
    "plt.ylabel('Precisão de passe em %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v74BLbtlHSPE"
   },
   "source": [
    "# Campeonatos ocorridos a partir de 2003\n",
    "\n",
    "A seguir, com a curiosidade de saber o desempenho geral de todo os times participantes do Brasileirão a partir do ano 2003 até 2021, iremos criar uma nota tabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "2klmlTSUf-4Y",
    "outputId": "ad73fc44-70a1-4c63-fad4-7488662d461a"
   },
   "outputs": [
    {
     "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>Clubes</th>\n",
       "      <th>J</th>\n",
       "      <th>PG</th>\n",
       "      <th>V</th>\n",
       "      <th>E</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>São paulo</td>\n",
       "      <td>704</td>\n",
       "      <td>1169</td>\n",
       "      <td>327</td>\n",
       "      <td>188</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santos</td>\n",
       "      <td>704</td>\n",
       "      <td>1097</td>\n",
       "      <td>305</td>\n",
       "      <td>182</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flamengo</td>\n",
       "      <td>704</td>\n",
       "      <td>1084</td>\n",
       "      <td>296</td>\n",
       "      <td>196</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cruzeiro</td>\n",
       "      <td>666</td>\n",
       "      <td>1030</td>\n",
       "      <td>293</td>\n",
       "      <td>151</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Internacional</td>\n",
       "      <td>666</td>\n",
       "      <td>1043</td>\n",
       "      <td>292</td>\n",
       "      <td>167</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grêmio</td>\n",
       "      <td>662</td>\n",
       "      <td>1022</td>\n",
       "      <td>283</td>\n",
       "      <td>173</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Corinthians</td>\n",
       "      <td>666</td>\n",
       "      <td>1035</td>\n",
       "      <td>281</td>\n",
       "      <td>192</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fluminense</td>\n",
       "      <td>704</td>\n",
       "      <td>999</td>\n",
       "      <td>270</td>\n",
       "      <td>189</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Atlético-mg</td>\n",
       "      <td>666</td>\n",
       "      <td>973</td>\n",
       "      <td>267</td>\n",
       "      <td>172</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Athlético-pr</td>\n",
       "      <td>666</td>\n",
       "      <td>955</td>\n",
       "      <td>266</td>\n",
       "      <td>157</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Palmeiras</td>\n",
       "      <td>620</td>\n",
       "      <td>960</td>\n",
       "      <td>266</td>\n",
       "      <td>162</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Botafogo-rj</td>\n",
       "      <td>620</td>\n",
       "      <td>812</td>\n",
       "      <td>213</td>\n",
       "      <td>173</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Vasco</td>\n",
       "      <td>590</td>\n",
       "      <td>765</td>\n",
       "      <td>196</td>\n",
       "      <td>177</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Goiás</td>\n",
       "      <td>514</td>\n",
       "      <td>685</td>\n",
       "      <td>189</td>\n",
       "      <td>118</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Coritiba</td>\n",
       "      <td>514</td>\n",
       "      <td>647</td>\n",
       "      <td>169</td>\n",
       "      <td>140</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Figueirense</td>\n",
       "      <td>438</td>\n",
       "      <td>550</td>\n",
       "      <td>142</td>\n",
       "      <td>124</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Vitória</td>\n",
       "      <td>396</td>\n",
       "      <td>468</td>\n",
       "      <td>123</td>\n",
       "      <td>99</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sport</td>\n",
       "      <td>380</td>\n",
       "      <td>462</td>\n",
       "      <td>122</td>\n",
       "      <td>96</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ponte preta</td>\n",
       "      <td>362</td>\n",
       "      <td>433</td>\n",
       "      <td>114</td>\n",
       "      <td>91</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>350</td>\n",
       "      <td>415</td>\n",
       "      <td>104</td>\n",
       "      <td>103</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Paraná</td>\n",
       "      <td>248</td>\n",
       "      <td>304</td>\n",
       "      <td>83</td>\n",
       "      <td>55</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Juventude</td>\n",
       "      <td>210</td>\n",
       "      <td>263</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Chapecoense</td>\n",
       "      <td>228</td>\n",
       "      <td>273</td>\n",
       "      <td>69</td>\n",
       "      <td>66</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>São caetano</td>\n",
       "      <td>172</td>\n",
       "      <td>236</td>\n",
       "      <td>65</td>\n",
       "      <td>41</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>202</td>\n",
       "      <td>236</td>\n",
       "      <td>61</td>\n",
       "      <td>53</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Avaí</td>\n",
       "      <td>228</td>\n",
       "      <td>236</td>\n",
       "      <td>57</td>\n",
       "      <td>65</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Náutico</td>\n",
       "      <td>190</td>\n",
       "      <td>200</td>\n",
       "      <td>54</td>\n",
       "      <td>38</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>190</td>\n",
       "      <td>221</td>\n",
       "      <td>54</td>\n",
       "      <td>59</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Atlético-go</td>\n",
       "      <td>190</td>\n",
       "      <td>206</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Criciúma</td>\n",
       "      <td>168</td>\n",
       "      <td>188</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Paysandu</td>\n",
       "      <td>134</td>\n",
       "      <td>154</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Guarani</td>\n",
       "      <td>130</td>\n",
       "      <td>147</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Portuguesa</td>\n",
       "      <td>114</td>\n",
       "      <td>131</td>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>América-mg</td>\n",
       "      <td>114</td>\n",
       "      <td>105</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Santa cruz</td>\n",
       "      <td>76</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Bragantino</td>\n",
       "      <td>38</td>\n",
       "      <td>53</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Barueri</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Santo andré</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Brasiliense</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Ipatinga</td>\n",
       "      <td>38</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Csa</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Grêmio prudente</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Joinville</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>América-rn</td>\n",
       "      <td>38</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Clubes    J    PG    V    E    D\n",
       "0         São paulo  704  1169  327  188  189\n",
       "1            Santos  704  1097  305  182  217\n",
       "2          Flamengo  704  1084  296  196  212\n",
       "3          Cruzeiro  666  1030  293  151  222\n",
       "4     Internacional  666  1043  292  167  207\n",
       "5            Grêmio  662  1022  283  173  206\n",
       "6       Corinthians  666  1035  281  192  193\n",
       "7        Fluminense  704   999  270  189  245\n",
       "8       Atlético-mg  666   973  267  172  227\n",
       "9      Athlético-pr  666   955  266  157  243\n",
       "10        Palmeiras  620   960  266  162  192\n",
       "11      Botafogo-rj  620   812  213  173  234\n",
       "12            Vasco  590   765  196  177  217\n",
       "13            Goiás  514   685  189  118  207\n",
       "14         Coritiba  514   647  169  140  205\n",
       "15      Figueirense  438   550  142  124  172\n",
       "16          Vitória  396   468  123   99  174\n",
       "17            Sport  380   462  122   96  162\n",
       "18      Ponte preta  362   433  114   91  157\n",
       "19            Bahia  350   415  104  103  143\n",
       "20           Paraná  248   304   83   55  110\n",
       "21        Juventude  210   263   71   50   89\n",
       "22      Chapecoense  228   273   69   66   93\n",
       "23      São caetano  172   236   65   41   66\n",
       "24        Fortaleza  202   236   61   53   88\n",
       "25             Avaí  228   236   57   65  106\n",
       "26          Náutico  190   200   54   38   98\n",
       "27            Ceará  190   221   54   59   77\n",
       "28      Atlético-go  190   206   51   53   86\n",
       "29         Criciúma  168   188   50   38   80\n",
       "30         Paysandu  134   154   41   31   62\n",
       "31          Guarani  130   147   36   39   55\n",
       "32       Portuguesa  114   131   31   38   45\n",
       "33       América-mg  114   105   25   30   59\n",
       "34       Santa cruz   76    59   15   14   47\n",
       "35       Bragantino   38    53   13   14   11\n",
       "36          Barueri   38    49   12   13   13\n",
       "37      Santo andré   38    41   11    8   19\n",
       "38      Brasiliense   42    42   10   12   20\n",
       "39         Ipatinga   38    35    9    8   21\n",
       "40              Csa   38    32    8    8   22\n",
       "41  Grêmio prudente   38    31    7   10   21\n",
       "42        Joinville   38    31    7   10   21\n",
       "43       América-rn   38    17    4    5   29"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nova analise a seguir\n",
    "dfNovo = dfDataChampionship\n",
    "#m = m.drop(['Data', 'Horário', 'Dia', 'Mandante', 'Visitante'], axis=1)\n",
    "\n",
    "\n",
    "filterNovo  =  dfNovo[\"Data\"]>='2003-01-01'   # inicio das pontos corridos\n",
    "filterNovo2  =  dfNovo[\"Data\"]<='2021-02-26'  # ate o ultimo campeonato finalizado?\n",
    "\n",
    "dfTeams = dfNovo[filterNovo & filterNovo2]\n",
    "\n",
    "clubs1  =  pd.concat([dfTeams['Mandante'].str.lower(), dfTeams['Visitante'].str.lower()], axis=1, keys=['Clubes'])\n",
    "cb1  =  pd.Series(clubs1['Clubes'].unique(), name=\"Clubes\") # unique retorna valores sem repetição\n",
    "cb1  =  cb1.to_frame() # convert Series to DataFrame \n",
    "cb1 # exibe o dataframe\n",
    "\n",
    "dfTabela = cb1[[\"Clubes\"]].copy()\n",
    "for column in [\"J\", \"PG\", \"V\", \"E\", \"D\"]:\n",
    "    dfTabela[column] = 0\n",
    "\n",
    "for index, row in dfTabela.iterrows():\n",
    "    c1  =  row['Clubes']\n",
    "    c1  =  ensureUtf( c1 )\n",
    "    c1  =  c1.strip()\n",
    "    \n",
    "    pg       =  getPoints(dfTeams, c1)\n",
    "    j        =  getNumberDepartures(dfTeams, c1 )\n",
    "    v        =  getNumberWinner(dfTeams, c1 )\n",
    "    e        =  getDraw(dfTeams, c1 )\n",
    "    d        =  getDefeats(dfTeams, c1 )\n",
    "    \n",
    "\n",
    "    dfTabela.at[index, 'J']   = j\n",
    "    dfTabela.at[index, 'PG']  = pg\n",
    "    dfTabela.at[index, 'V']   = v\n",
    "    dfTabela.at[index, 'E']   = e\n",
    "    dfTabela.at[index, 'D']   = d\n",
    "\n",
    "dfTabela['Clubes'] = dfTabela['Clubes'].str.capitalize() \n",
    "dfTabela  =  dfTabela.sort_values(by=['V'], ascending=False) # ordena os valores\n",
    "dfTabela  =  dfTabela.reset_index(drop=True)\n",
    "\n",
    "dfTabela"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lXrlOTps6SsT"
   },
   "source": [
    "Iremos escolher, aleatoriamente, dois times da tabela acima para fazer uma comparação visual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "id": "0GZ1t8ET1TDb",
    "outputId": "01d515a2-7b3f-48d4-8723-526c4b6a11cf"
   },
   "outputs": [
    {
     "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>Clubes</th>\n",
       "      <th>J</th>\n",
       "      <th>PG</th>\n",
       "      <th>V</th>\n",
       "      <th>E</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>202</td>\n",
       "      <td>236</td>\n",
       "      <td>61</td>\n",
       "      <td>53</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>190</td>\n",
       "      <td>221</td>\n",
       "      <td>54</td>\n",
       "      <td>59</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clubes    J   PG   V   E   D\n",
       "24  Fortaleza  202  236  61  53  88\n",
       "27      Ceará  190  221  54  59  77"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCeFor = dfTabela.loc[(dfTabela['Clubes'].str.contains('Ceará') | dfTabela['Clubes'].str.contains('Fortaleza'))]\n",
    "dfCeFor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "F9wt5HEA9DLJ",
    "outputId": "4c3deb4e-657e-4cd7-8a7c-cc81148441cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAI/CAYAAAC1XpeNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAghUlEQVR4nO3dfZRdZWHv8d9DAgEhReRNBTTAQl4TJjiJICYmrSX4cnmp+JK+AIIiVvS6atOqXRJQvKUCXopcRVx6iV0ooSLKVahWJAIqJUM6hJAgJII1iBBQkihESHjuHzmkAwbyMs8wk+TzWWvWnPPsffZ+zmStk+/sveecUmsNAAD9t9VgTwAAYHMhrAAAGhFWAACNCCsAgEaEFQBAI8IKAKCR4YM9gSTZZZdd6qhRowZ7GgAA63Tbbbc9XGvddW3LhkRYjRo1Kj09PYM9DQCAdSql/Py5ljkVCADQiLACAGhEWAEANDIkrrECAF5YTz75ZBYvXpwVK1YM9lSGrG233TZ77rlntt566/V+jLACgC3Q4sWLM3LkyIwaNSqllMGezpBTa80jjzySxYsXZ++9917vxzkVCABboBUrVmTnnXcWVc+hlJKdd955g4/oCSsA2EKJque3MT8fYQUADIphw4alq6srhxxySN72trflscce2+BtPProo/nc5z43ALPbOMIKAEgpbb/Wx3bbbZfe3t7Mmzcv22yzTS655JINnrewAgB4lgkTJmThwoX59a9/neOOOy5jxozJ4Ycfnrlz5yZJzjrrrJxyyimZNGlS9tlnn1x00UVJko985CNZtGhRurq6Mm3atNRaM23atBxyyCEZPXp0Zs6cmSR54IEHMnHixDVHyG666aYBeR7+KhAAGFQrV67Mddddl6OPPjrTp0/P2LFj881vfjM/+MEPcuKJJ6a3tzdJctddd+WGG27I8uXLs//+++d973tfzj333MybN2/NOldddVV6e3tz++235+GHH864ceMyceLEfPWrX82UKVPyD//wD1m1atVGnXZcH8IKABgUjz/+eLq6upKsPmJ16qmn5jWveU2uuuqqJMkf//Ef55FHHsmyZcuSJG9+85szYsSIjBgxIrvttlsefPDBP9jmzTffnKlTp2bYsGHZfffd8/rXvz6zZ8/OuHHjcsopp+TJJ5/Mcccdt2a/rQkrAGBQPH2N1foaMWLEmtvDhg3LypUr1/uxEydOzI033pjvfOc7Ofnkk/M3f/M3OfHEEzdkuuvFNVYAwJAxYcKEXH755UmSWbNmZZdddskf/dEfPef6I0eOzPLly5/x+JkzZ2bVqlVZsmRJbrzxxowfPz4///nPs/vuu+c973lP3v3ud2fOnDkDMn9HrACAIePpi9THjBmTF73oRZkxY8bzrr/zzjvnyCOPzCGHHJI3vvGN+fSnP52f/OQnOfTQQ1NKyac//em89KUvzYwZM3Leeedl6623zg477JCvfOUrAzL/UmsdkA1viO7u7trT0zPY0wCALcaCBQty4IEHDvY0hry1/ZxKKbfVWrvXtr5TgQAAjQgrAIBGhBUAQCPCCgCgEWEFANCIsAIAaERYAQAvuMmTJ+e73/3uM8YuvPDC7L333jn33HOTJN/85jczf/78Dd72JZdcMmDvU7Uu3iAUAEg5uzTdXp3+/O+TOXXq1FxxxRWZMmXKmrErrrgiM2bMyMSJE5OsDqu3vOUtOeigg9Z7vytXrszpp5++cZNuYIs6YlXK5v0FAJuKE044Id/5znfyxBNPJEnuu+++/PKXv8yiRYtyxhln5Mc//nGuueaaTJs2LV1dXVm0aFF6e3tz+OGHZ8yYMTn++OPzm9/8JkkyadKkfOhDH0p3d3f++Z//OWeddVbOP//8JMkXv/jFjBs3Loceemje+ta35rHHHhvQ57VFhRUAMDS85CUvyfjx43PdddclWX206u1vf3tK50jBa1/72hxzzDE577zz0tvbm3333Tcnnnhi/umf/ilz587N6NGjc/bZZ6/Z3hNPPJGenp58+MMffsZ+/uzP/iyzZ8/O7bffngMPPDBf+tKXBvR5CSsAYFA8fTowWR1WU6dOfc51ly5dmkcffTSvf/3rkyQnnXRSbrzxxjXL3/GOd6z1cfPmzcuECRMyevToXH755bnzzjsbPoM/JKwAgEFx7LHH5vrrr8+cOXPy2GOP5dWvfvVGb2v77bdf6/jJJ5+ciy++OHfccUemT5+eFStWbPQ+1oewAgAGxQ477JDJkyfnlFNOWevRqpEjR2b58uVJkh133DE77bRTbrrppiTJv/zLv6w5evV8li9fnpe97GV58sknc/nll7d9AmshrACAQTN16tTcfvvtaw2rd77znTnvvPMyduzYLFq0KDNmzMi0adMyZsyY9Pb25swzz1zn9j/5yU/mNa95TY488sgccMABA/EUnqHU+vx/DvlC6O7urj09PQO+n839L+eGwD8lAJuIBQsW5MADDxzsaQx5a/s5lVJuq7V2r219R6wAABoRVgAAjQgrAIBGhBUAQCPCCgCgEWEFANCIsAIABsWwYcPS1dW15uvcc88d0P1deOGFA/4hzMMHdOsAwCah7wcatzB9+vR1rrPddtult7e36X6fz4UXXpi//Mu/zIte9KIB24cjVgDAkDJq1Kh89KMfTVdXV7q7uzNnzpxMmTIl++67by655JIkyaxZszJx4sS8+c1vzv7775/TTz89Tz31VJLkfe97X7q7u3PwwQevCbyLLroov/zlLzN58uRMnjw5SfK9730vRxxxRA477LC87W1vy29/+9t+z11YAQCD4vHHH3/GqcCZM2euWfaKV7wivb29mTBhQk4++eR8/etfzy233PKMI2G33nprPvvZz2b+/PlZtGhRvvGNbyRJPvWpT6Wnpydz587ND3/4w8ydOzcf/OAH8/KXvzw33HBDbrjhhjz88MM555xz8v3vfz9z5sxJd3d3PvOZz/T7OTkVCAAMiuc7FXjMMcckSUaPHp3f/va3GTlyZEaOHJkRI0bk0UcfTZKMHz8+++yzT5LVnzl4880354QTTsiVV16ZSy+9NCtXrswDDzyQ+fPnZ8yYMc/Y/i233JL58+fnyCOPTJI88cQTOeKII/r9nIQVADDkjBgxIkmy1VZbrbn99P2VK1cmScqzPgS4lJJ77703559/fmbPnp2ddtopJ598clasWPEH26+15k//9E/zta99rem8nQoEADZJt956a+6999489dRTmTlzZl73utdl2bJl2X777bPjjjvmwQcfzHXXXbdm/ZEjR2b58uVJksMPPzw/+tGPsnDhwiTJ7373u9x99939npMjVgDAoHj6GqunHX300Rv0lgvjxo3LGWeckYULF2by5Mk5/vjjs9VWW2Xs2LE54IADstdee6051Zckp512Wo4++ug111pddtllmTp1an7/+98nSc4555y86lWv6tdzKrXWfm2ghe7u7trT0zPg+3nWEcPNzhD4pwRgE7FgwYIceOCBgz2NjTZr1qycf/75+fa3vz2g+1nbz6mUcluttXtt6zsVCADQiFOBAMAmZ9KkSZk0adJgT+MPOGIFANCIsAIAaERYAQA0IqwAABoRVgDAoBg2bFi6urpy8MEH59BDD80FF1yw5oOUW+vt7c211147INvuy18FAgDt3+xxPd5cse9nBT700EP58z//8yxbtixnn332eu1i5cqVGT58+HPe76u3tzc9PT1505vetF7b3liOWAEAg2633XbLpZdemosvvji11qxatSrTpk3LuHHjMmbMmHzhC19IsvqNQSdMmJBjjjkmBx100B/cX7FiRd71rndl9OjRGTt2bG644YY88cQTOfPMMzNz5sx0dXVl5syZufXWW3PEEUdk7Nixee1rX5uf/vSnTZ6HI1YAwJCwzz77ZNWqVXnooYfyrW99KzvuuGNmz56d3//+9znyyCNz1FFHJUnmzJmTefPmZe+9986sWbOecf+CCy5IKSV33HFH7rrrrhx11FG5++6784lPfCI9PT25+OKLkyTLli3LTTfdlOHDh+f73/9+Pvaxj+Wqq67q93MQVgDAkPO9730vc+fOzde//vUkydKlS3PPPfdkm222yfjx47P33nuvWbfv/Ztvvjkf+MAHkiQHHHBAXvnKV671w5WXLl2ak046Kffcc09KKXnyySebzFtYATSyuX8eaeIzSRlYP/vZzzJs2LDstttuqbXms5/9bKZMmfKMdWbNmpXtt9/+GWPPvr8+Pv7xj2fy5Mm5+uqrc9999zV7F3fXWAEAg27JkiU5/fTTc8YZZ6SUkilTpuTzn//8miNJd999d373u9+tczsTJkzI5ZdfvuYx//Vf/5X9998/I0eOzPLly9est3Tp0uyxxx5Jkssuu6zZ8xBWAMCgePzxx9e83cIb3vCGHHXUUZk+fXqS5N3vfncOOuigHHbYYTnkkEPy3ve+NytXrlznNv/6r/86Tz31VEaPHp13vOMdueyyyzJixIhMnjw58+fPX3Px+t/93d/lox/9aMaOHbte211fpQ6B47rd3d21p6dnwPezuR+mHwL/lLBF29xfYxKvM5uTBQsW5MADDxzsaQx5a/s5lVJuq7V2r219R6wAABoRVgAAjQgrAIBGhBUAbKGGwnXWQ9nG/HyEFQBsgbbddts88sgj4uo51FrzyCOPZNttt92gx3mDUADYAu25555ZvHhxlixZMthTGbK23Xbb7Lnnnhv0GGEFAFugrbfe+hkfC0MbTgUCADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0Mg6w6qUslcp5YZSyvxSyp2llP/ZGX9JKeXfSyn3dL7v1BkvpZSLSikLSylzSymHDfSTAAAYCtbniNXKJB+utR6U5PAk7y+lHJTkI0mur7Xul+T6zv0keWOS/TpfpyX5fPNZAwAMQesMq1rrA7XWOZ3by5MsSLJHkmOTzOisNiPJcZ3bxyb5Sl3tliQvLqW8rPXEAQCGmg26xqqUMirJ2CT/kWT3WusDnUW/SrJ75/YeSX7R52GLO2MAAJu19Q6rUsoOSa5K8qFa67K+y2qtNUndkB2XUk4rpfSUUnqWLFmyIQ8FABiS1iusSilbZ3VUXV5r/UZn+MGnT/F1vj/UGb8/yV59Hr5nZ+wZaq2X1lq7a63du+6668bOHwBgyFifvwosSb6UZEGt9TN9Fl2T5KTO7ZOSfKvP+Imdvw48PMnSPqcMAQA2W8PXY50jk/xVkjtKKb2dsY8lOTfJlaWUU5P8PMnbO8uuTfKmJAuTPJbkXS0nDAAwVK0zrGqtNycpz7H4T9ayfk3y/n7OCwBgk+Od1wEAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKCRdYZVKeXLpZSHSinz+oydVUq5v5TS2/l6U59lHy2lLCyl/LSUMmWgJg4AMNSszxGry5IcvZbx/11r7ep8XZskpZSDkrwzycGdx3yulDKs1WQBAIaydYZVrfXGJL9ez+0dm+SKWuvva633JlmYZHw/5gcAsMnozzVWZ5RS5nZOFe7UGdsjyS/6rLO4MwYAsNnb2LD6fJJ9k3QleSDJBRu6gVLKaaWUnlJKz5IlSzZyGgAAQ8dGhVWt9cFa66pa61NJvpj/Pt13f5K9+qy6Z2dsbdu4tNbaXWvt3nXXXTdmGgAAQ8pGhVUp5WV97h6f5Om/GLwmyTtLKSNKKXsn2S/Jrf2bIgDApmH4ulYopXwtyaQku5RSFieZnmRSKaUrSU1yX5L3Jkmt9c5SypVJ5idZmeT9tdZVAzJzAIAhptRaB3sO6e7urj09PQO+n1IGfBeDagj8U8IWbXN/jUm8zkCSlFJuq7V2r22Zd14HAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI2sM6xKKV8upTxUSpnXZ+wlpZR/L6Xc0/m+U2e8lFIuKqUsLKXMLaUcNpCTBwAYStbniNVlSY5+1thHklxfa90vyfWd+0nyxiT7db5OS/L5NtMEABj61hlWtdYbk/z6WcPHJpnRuT0jyXF9xr9SV7slyYtLKS9rNFcAgCFtY6+x2r3W+kDn9q+S7N65vUeSX/RZb3FnDABgs9fvi9drrTVJ3dDHlVJOK6X0lFJ6lixZ0t9pAAAMuo0NqwefPsXX+f5QZ/z+JHv1WW/PztgfqLVeWmvtrrV277rrrhs5DQCAoWNjw+qaJCd1bp+U5Ft9xk/s/HXg4UmW9jllCACwWRu+rhVKKV9LMinJLqWUxUmmJzk3yZWllFOT/DzJ2zurX5vkTUkWJnksybsGYM4AAEPSOsOq1jr1ORb9yVrWrUne399JAQBsirzzOgBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhk+2BMA+ihlsGcwsGod7BkADChHrAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoZPhgTwAAeIGUMtgzGHi1DuruHbECAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANCKsAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANDJ8sCdAO+XsMthTGFB1eh3sKQDA83LECgCgEWEFANCIsAIAaMQ1VgDQcfbZZw/2FAbU9MGewBbAESsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAIz7SBoD1Vs4ugz2FAXVWzhrsKbCJc8QKAKARYQUA0IhTgWwyNvdPnU988jzAps4RKwCARvp1xKqUcl+S5UlWJVlZa+0upbwkycwko5Lcl+Tttdbf9G+aAABDX4sjVpNrrV211u7O/Y8kub7Wul+S6zv3AQA2ewNxKvDYJDM6t2ckOW4A9gEAMOT0N6xqku+VUm4rpZzWGdu91vpA5/avkuzez30AAGwS+vtXga+rtd5fStktyb+XUu7qu7DWWkspdW0P7ITYaUnyile8op/TAAAYfP06YlVrvb/z/aEkVycZn+TBUsrLkqTz/aHneOyltdbuWmv3rrvu2p9pAAAMCRsdVqWU7UspI5++neSoJPOSXJPkpM5qJyX5Vn8nCQCwKejPqcDdk1xdSnl6O1+ttf5bKWV2kitLKacm+XmSt/d/mgAAQ99Gh1Wt9WdJDl3L+CNJ/qQ/kwIA2BR553UAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoRFgBADQirAAAGhFWAACNCCsAgEaEFQBAI8IKAKARYQUA0IiwAgBoZMDCqpRydCnlp6WUhaWUjwzUfgAAhooBCatSyrAk/yfJG5MclGRqKeWggdgXAMBQMVBHrMYnWVhr/Vmt9YkkVyQ5doD2BQAwJAxUWO2R5Bd97i/ujAEAbLZKrbX9Rks5IcnRtdZ3d+7/VZLX1FrP6LPOaUlO69zdP8lPm08E+meXJA8P9iSAzZrXmU3TK2utu65twfAB2uH9Sfbqc3/PztgatdZLk1w6QPuHfiul9NRauwd7HsDmy+vM5megTgXOTrJfKWXvUso2Sd6Z5JoB2hcAwJAwIEesaq0rSylnJPlukmFJvlxrvXMg9gUAMFQM1KnA1FqvTXLtQG0fXgBOVQMDzevMZmZALl4HANgS+UgbANgElFIOLqX8j8GeB89PWLFZKKWsKqX09vkatQGPPW59PhmglHJWKeVv+zVRYLNVSnlpKeWKUsqiUsptpZRrSymvarTt4UkuSDKnxfYYOAN2jRW8wB6vtXZt6IM6L1bHJfl2kvmN5wRsIUopJcnVSWbUWt/ZGTs0ye5J7u7HdofVWlcl2S/J9Frr/et6DIPLESs2W6WUrlLKLaWUuaWUq0spO3XGZ5VSLiyl9CT5+yTHJDmvc6Rr31LKe0ops0spt5dSriqlvGgt2963lPJvnd9KbyqlHNAZ73vU7PFSyutLKeNLKT8ppfxnKeXHpZT9X9AfBPBCmJzkyVrrJU8P1Fpvr7XeVEqZ1nlNmVtKOfvp5aWUb3ZeQ+7svGn20+O/LaVcUEq5PckRpZQzk3wlyZdKKZd2Io4hSlixudiuT9Bc3Rn7SpK/r7WOSXJHkul91t+m1tpda/1UVr/H2rRaa1etdVGSb9Rax9VaD02yIMmpa9nfpUk+UGt9dZK/TfK5JOlsoyvJx5P0JPlxkruSTKi1jk1yZpL/1fapA0PAIUlue/ZgKeWorD7aND5JV5JXl1Imdhaf0nkN6U7ywVLKzp3x7ZP8R6310FrrzUkurrWOSzI6yQ5J3jKgz4R+cSqQzcUzTgWWUnZM8uJa6w87QzOS/Guf9Wc+z7YOKaWck+TFWf0i9t2+C0spOyR5bZJ/7fOL44g+y/dLcl6SybXWJ0spL00yozNek2y9wc8O2FQd1fn6z879HbI6tG7M6pg6vjO+V2f8kSSrklzVZxsTSikfyur/s1+eZG6S/zfgM2ejCCu2VL97nmWXJTmu1np7KeXkJJOetXyrJI+u7ZquTnRdmeQ9tdYHOsOfTHJDrfX4zkX1s/ozcWBIujPJCWsZL0n+sdb6hWcMljIpyRuSHFFrfayUMivJtp3FKzrXVaWUMiLJF5J01Vp/1TmVuG0YspwKZLNUa12a5DellAmdob9K8sPnWH15kpF97o9M8kApZeskf7GWbS9Lcm8p5W3J6otWOxepJsmXk/zfWutNfR6yY/77szJP3oinAwx9P0gy4lnXSo1JsizJKZ1fulJK2aOUsltWvy78phNVByQ5/Dm2u11W/1+9tLONtw7kk6D/hBWbs5Oy+qL0uVl9bcMnnmO9K5JM61xcvm9WXx/1H0l+lNXXR63NXyQ5tXNx6Z1Jji2lvDKrf2M9pc/1Xt1JPp3kH0sp/xlHiWGzVFe/2/bxSd7QebuFO5P8Y5Kvdr5+Ukq5I8nXs/qXt39LMryUsiDJuUlueY7tPprkS0nmZfVlCbMH+KnQT955HQCgEUesAAAaEVYAAI0IKwCARoQVAEAjwgoAoBFhBQDQiLACAGhEWAEANPL/Aagx/OIj+3TVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "Pos = np.array(range(len(dfCeFor)))\n",
    "width = 0.2\n",
    "\n",
    "plt.bar(Pos-2*width, dfCeFor['PG'], width=width, color='blue', label='Pontos')\n",
    "plt.bar(Pos-width, dfCeFor['V'], width=width, color='green', label='Vitoria')\n",
    "plt.bar(Pos, dfCeFor['E'], width=width, color='gray', label='Empate')\n",
    "plt.bar(Pos+width, dfCeFor['D'], width=width, color='red', label='Derrota')\n",
    "\n",
    "plt.xticks(Pos, dfCeFor['Clubes'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "Analisamos, dentro do intervalo de data definido anteriormente (2003-2020), que há uma vantagem de pontos do Tricolor do Pici (Fortaleza) sobre o seu rival (Ceará). \n",
    "\n",
    "Entretanto, vale ressaltar que o Fortaleza jogou 202 vezes, enquanto que o Ceará jogou apenas 190 vezes. Assim, para se obter uma análise mais precisa, deveríamos levar em consideração a proporção de vezes que cada time jogou, entre outros fatores. "
   ]
  },
  {
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   "metadata": {
    "id": "SMZ047Lc7zf7"
   },
   "source": [
    " "
   ]
  }
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