treinadores_final_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f47810c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando as bibliotecas necessarias \n",
    "import sqlite3 as sql\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d423363",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conectar o BD sqlite ao Pandas\n",
    "\n",
    "conn = sql.connect('treinadores.db')\n",
    "treinadores = pd.read_sql('SELECT * FROM treinadores', conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "de7bc97d",
   "metadata": {},
   "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>Ano</th>\n",
       "      <th>Treinador</th>\n",
       "      <th>Time</th>\n",
       "      <th>Nacionalidade</th>\n",
       "      <th>Mesesnocomando</th>\n",
       "      <th>Aproveitamento</th>\n",
       "      <th>Títulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>Darío Pereyra</td>\n",
       "      <td>Grêmio</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>1</td>\n",
       "      <td>20,83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>Darío Pereyra</td>\n",
       "      <td>Paysandu</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>5</td>\n",
       "      <td>64,8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>Roberto Rojas</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Chileno</td>\n",
       "      <td>7</td>\n",
       "      <td>60,7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>Hugo De León</td>\n",
       "      <td>Grêmio</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>4</td>\n",
       "      <td>47,6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>D. Passarella</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>Argentino</td>\n",
       "      <td>2</td>\n",
       "      <td>55,6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>Matthaus</td>\n",
       "      <td>Athletico-PR</td>\n",
       "      <td>Alemão</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ano      Treinador          Time Nacionalidade  Mesesnocomando  \\\n",
       "0  2003  Darío Pereyra        Grêmio      Uruguaio               1   \n",
       "1  2003  Darío Pereyra      Paysandu      Uruguaio               5   \n",
       "2  2003  Roberto Rojas     São Paulo       Chileno               7   \n",
       "3  2004   Hugo De León        Grêmio      Uruguaio               4   \n",
       "4  2005  D. Passarella   Corinthians     Argentino               2   \n",
       "5  2006       Matthaus  Athletico-PR        Alemão               2   \n",
       "\n",
       "  Aproveitamento  Títulos  \n",
       "0          20,83        0  \n",
       "1           64,8        0  \n",
       "2           60,7        0  \n",
       "3           47,6        0  \n",
       "4           55,6        0  \n",
       "5             83        0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lendo uma amostra do dataset\n",
    "treinadores.head(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "493cfb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renomear algumas colunas\n",
    "treinadores = treinadores.rename(columns={'Mesesnocomando': 'Meses_No_Comando'})\n",
    "treinadores = treinadores.rename(columns={'Títulos': 'Titulos'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e9e03064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano                  int64\n",
       "Treinador           object\n",
       "Time                object\n",
       "Nacionalidade       object\n",
       "Meses_No_Comando     int64\n",
       "Aproveitamento      object\n",
       "Titulos              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Procurando problemas nas variaveis \n",
    "treinadores.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f61f36b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparação dos dados\n",
    "treinadores = treinadores.replace({',': '.'}, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4f372c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comandos para remover outliers do dataset\n",
    "\n",
    "#treinadores.duplicated()\n",
    "# treinadores.dropna() # Encontrar valores nulos\n",
    "# treinadores.fillna(colocar os valores aqui) # Preencher todos os valores NaN por um outro específico "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ff847e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano                  int64\n",
       "Treinador           object\n",
       "Time                object\n",
       "Nacionalidade       object\n",
       "Meses_No_Comando     int64\n",
       "Aproveitamento      object\n",
       "Titulos              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Procurando problemas nas variaveis \n",
    "treinadores.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "120f2000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converter 'Aproveitamento' de objeto p/ ponto flutante  \n",
    "treinadores[\"Aproveitamento\"] = treinadores[\"Aproveitamento\"].astype(str).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac3c9f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ano                   int64\n",
       "Treinador            object\n",
       "Time                 object\n",
       "Nacionalidade        object\n",
       "Meses_No_Comando      int64\n",
       "Aproveitamento      float64\n",
       "Titulos               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conferindo\n",
    "treinadores.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "837aeab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Ano</th>\n",
       "      <th>Treinador</th>\n",
       "      <th>Time</th>\n",
       "      <th>Nacionalidade</th>\n",
       "      <th>Meses_No_Comando</th>\n",
       "      <th>Aproveitamento</th>\n",
       "      <th>Titulos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>Darío Pereyra</td>\n",
       "      <td>Grêmio</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>1</td>\n",
       "      <td>20.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>Darío Pereyra</td>\n",
       "      <td>Paysandu</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>5</td>\n",
       "      <td>64.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>Roberto Rojas</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Chileno</td>\n",
       "      <td>7</td>\n",
       "      <td>60.70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>Hugo De León</td>\n",
       "      <td>Grêmio</td>\n",
       "      <td>Uruguaio</td>\n",
       "      <td>4</td>\n",
       "      <td>47.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>D. Passarella</td>\n",
       "      <td>Corinthians</td>\n",
       "      <td>Argentino</td>\n",
       "      <td>2</td>\n",
       "      <td>55.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ano      Treinador         Time Nacionalidade  Meses_No_Comando  \\\n",
       "0  2003  Darío Pereyra       Grêmio      Uruguaio                 1   \n",
       "1  2003  Darío Pereyra     Paysandu      Uruguaio                 5   \n",
       "2  2003  Roberto Rojas    São Paulo       Chileno                 7   \n",
       "3  2004   Hugo De León       Grêmio      Uruguaio                 4   \n",
       "4  2005  D. Passarella  Corinthians     Argentino                 2   \n",
       "\n",
       "   Aproveitamento  Titulos  \n",
       "0           20.83        0  \n",
       "1           64.80        0  \n",
       "2           60.70        0  \n",
       "3           47.60        0  \n",
       "4           55.60        0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conferindo o resultado das transformações\n",
    "treinadores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "04107afd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Ano com o maior nº de treindadores'}>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Ano\"].value_counts().sort_values().plot.bar(title=\"Ano com o maior nº de treindadores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0cdc06ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Nacionalidade\"].value_counts().plot.pie(ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "75ce6ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Treinador'}>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Treinador\"].value_counts().sort_values().plot.bar(title=\"Treinador\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43b51b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Clubes com o maior nº de Treinadores'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Time\"].value_counts().sort_values().plot.bar(title=\"Clubes com o maior nº de Treinadores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb57c10f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(treinadores,columns=['Treinador','Aproveitamento'])\n",
    "ax = df.plot.bar(x = 'Treinador', y= 'Aproveitamento')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87c61395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Titulos\"].value_counts().sort_values().plot.bar(ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fecb5589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(treinadores,columns=['Treinador','Titulos'])\n",
    "\n",
    "df.groupby(['Treinador']).sum().plot(kind='pie', y='Titulos', ylabel='')\n",
    "plt.legend().remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d42b9a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(treinadores,columns=['Treinador','Meses_No_Comando'])\n",
    "ax = df.plot.bar(x = 'Treinador', y= 'Meses_No_Comando')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c765be18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores = pd.DataFrame(treinadores,columns=['Meses_No_Comando','Aproveitamento'])\n",
    "\n",
    "# Load data into DataFrame\n",
    "df = pd.DataFrame(treinadores);\n",
    "\n",
    "_ = sns.lmplot(x = 'Meses_No_Comando', y= 'Aproveitamento', data=df, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "652bb8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores = pd.DataFrame(treinadores,columns=['Aproveitamento', 'Titulos'])\n",
    "\n",
    "# Load data into DataFrame\n",
    "df = pd.DataFrame(treinadores);\n",
    "\n",
    "_ = sns.lmplot(x = 'Aproveitamento', y= 'Titulos', data=df, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8176223a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6rtUiSHJmknuSbEnyjiHr35RkPsmm5vbWNvNIkp6qzWsWLwH+CDgD2AZ8Pcn6qrprYOi1VXVBWzkkSYtrc4vgFGBLVX2rqh4DPgGc0+LzSZL2QZtFcBSwtW9+W7Ns0GuS3JbkuiSrhj1QkrVJ5pLMzc/Pt5FVkjpr0m8Wfx5YU1XHAzcAVw8bVFXrqmq2qmZXrFgx1oCSdKBrswi2A/1/4c80y36qqh6sqkeb2Q8DL2kxjyRpiDaL4OvAsUmOSfIs4Dxgff+AJCv7Zs8GNreYR5I0RGtHDVXVriQXAF8ElgBXVdWdSS4D5qpqPfC2JGcDu4CHgDe1lUeSNFxrRQBQVdcD1w8su7Rv+hLgkjYzSJIWN+k3iyVJE2YRSFLHWQSS1HEWgSR1nEUgSR1nEUhSx1kEktRxFoEkdZxFIEkdZxFIUsdZBJLUcRaBJHWcRSBJHWcRSFLHWQSS1HEWgSR1nEUgSR3XahEkOTPJPUm2JHnHkPUHJbm2WX9TkjVt5pEkPVVrRZBkCfBHwKuAFwGvS/KigWFvAb5fVS8ArgAubyuPJGm4NrcITgG2VNW3quox4BPAOQNjzgGubqavA16ZJC1mkiQNSFW188DJucCZVfXWZv4NwMuq6oK+MXc0Y7Y183/XjPnewGOtBdY2s8cB97QSumc58L09jpqcac8H059x2vPB9Gec9nxgxkFHV9WKYSuWjinA01JV64B143iuJHNVNTuO59oX054Ppj/jtOeD6c847fnAjHujzV1D24FVffMzzbKhY5IsBY4AHmwxkyRpQJtF8HXg2CTHJHkWcB6wfmDMeuCNzfS5wFeqrX1VkqShWts1VFW7klwAfBFYAlxVVXcmuQyYq6r1wJXANUm2AA/RK4tJG8suqKdh2vPB9Gec9nww/RmnPR+YcWStvVksSdo/+MliSeo4i0CSOq6zRZDkqiQ7ms8y7F72nCQ3JPlm8+/PTTjjqiQ3JrkryZ1JLpymnEkOTnJzklubfO9ulh/TnDJkS3MKkWdNIl9fziVJvpFkw5TmuzfJ7Uk2JZlrlk3F97gv45FJrktyd5LNSV4+TRmTHNe8frtvO5NcNGUZ/2vze3JHko83vz9T8bPY2SIAPgKcObDsHcCXq+pY4MvN/CTtAt5eVS8CTgXOb07TMS05HwVOr6oTgBOBM5OcSu9UIVc0pw75Pr1TiUzShcDmvvlpywfwK1V1Yt8x5dPyPd7tD4E/r6oXAifQez2nJmNV3dO8ficCLwF+BHx2WjImOQp4GzBbVS+mdwDNeUzLz2JVdfYGrAHu6Ju/B1jZTK8E7pl0xoG8/xc4YxpzAocCtwAvo/dJyaXN8pcDX5xgrhl6/wGcDmwAMk35mgz3AssHlk3N95je53u+TXNwyTRmHMj1b4G/naaMwFHAVuA59I7W3AD82rT8LHZ5i2CY51XV/c30A8DzJhmmX3Nm1pOAm5iinM1ul03ADuAG4O+AH1TVrmbINnq/BJPyXuD3gJ80889luvIBFPAXSTY2p1OBKfoeA8cA88CfNrvYPpzkMKYrY7/zgI8301ORsaq2A/8L+A5wP/AwsJEp+Vm0CBZQvYqeimNrkzwb+DRwUVXt7F836ZxV9UT1Nsdn6J1o8IWTyjIoyVnAjqraOOkse/BLVXUyvTP1np/kX/evnPT3mN5fsCcDH6yqk4AfMrCLZQoyAtDsYz8b+NTguklmbN6bOIdeqf5z4DCeumt6YiyCJ/v7JCsBmn93TDgPSZbRK4GPVdVnmsVTl7OqfgDcSG/z9sjmlCEw/NQi4/IK4Owk99I7++3p9PZ1T0s+4Kd/LVJVO+jt1z6F6foebwO2VdVNzfx19IphmjLu9irglqr6+2Z+WjL+KvDtqpqvqseBz9D7+ZyKn0WL4Mn6T3nxRnr75CcmSeh9+npzVb2nb9VU5EyyIsmRzfQh9N6/2EyvEM6ddL6quqSqZqpqDb3dBV+pqt+elnwASQ5LcvjuaXr7t+9gSr7HAFX1ALA1yXHNolcCdzFFGfu8jn/aLQTTk/E7wKlJDm1+r3e/htPxsziJNyam4Ubvh+V+4HF6f/G8hd7+4y8D3wS+BDxnwhl/id6m7G3Apub26mnJCRwPfKPJdwdwabP854GbgS30NtEPmoLv92nAhmnL12S5tbndCfz3ZvlUfI/7cp4IzDXf688BPzeFGQ+jd9LKI/qWTU1G4N3A3c3vyjXAQdPys+gpJiSp49w1JEkdZxFIUsdZBJLUcRaBJHWcRSBJHWcRSFLHWQSS1HH/HyfOACII6PtMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "treinadores[\"Aproveitamento\"].plot.hist(bins=35, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "340cf9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Uruguaio', 'Chileno', 'Argentino', 'Alemão', 'Sérvio', 'Espanhol',\n",
       "       'Colombiano', 'Português', 'Paraguaio', 'Venezuelano'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adicionando uma nova coluna na tabela treinadores\n",
    "nacionalidade = treinadores[\"Nacionalidade\"].unique()\n",
    "nacionalidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e74f4cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agrupamentos\n",
    "gender_total_item_price = treinadores.groupby([\"Nacionalidade\"]).mean()[\"Meses_No_Comando\"]\n",
    "gender_b_item_price = treinadores.groupby([\"Nacionalidade\"]).mean()[\"Aproveitamento\"]\n",
    "gender_a_item_price = treinadores.groupby([\"Nacionalidade\"]).sum()[\"Titulos\"]\n",
    "purchase_count = treinadores.groupby([\"Nacionalidade\"]).count()[\"Titulos\"]\n",
    "\n",
    "# Armazenando o resultado em um Dataframe\n",
    "gender_purchasing_analysis = pd.DataFrame({\"Quantidade\" : purchase_count, \n",
    "                                           \"Aproveitamento médio\" : gender_b_item_price,\n",
    "                                           \"Títulos\" : gender_a_item_price,\n",
    "                                           \"Médio tempo no comando\" : gender_total_item_price})\n",
    "\n",
    "# Data Munging\n",
    "gender_purchasing_analysis = gender_purchasing_analysis.round(2)\n",
    "gender_purchasing_analysis [\"Médio tempo no comando\"] = gender_purchasing_analysis[\"Médio tempo no comando\"].map(\"{:,.2f}\".format)\n",
    "gender_purchasing_analysis [\"Títulos\"] = gender_purchasing_analysis[\"Títulos\"].map(\"{:}\".format)\n",
    "gender_purchasing_analysis [\"Aproveitamento médio\"] = gender_purchasing_analysis[\"Aproveitamento médio\"].map(\"{:,.2f}\".format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8eacf44b",
   "metadata": {},
   "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>Quantidade</th>\n",
       "      <th>Aproveitamento médio</th>\n",
       "      <th>Títulos</th>\n",
       "      <th>Médio tempo no comando</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nacionalidade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alemão</th>\n",
       "      <td>1</td>\n",
       "      <td>83.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentino</th>\n",
       "      <td>10</td>\n",
       "      <td>49.24</td>\n",
       "      <td>3</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chileno</th>\n",
       "      <td>1</td>\n",
       "      <td>60.70</td>\n",
       "      <td>0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombiano</th>\n",
       "      <td>1</td>\n",
       "      <td>51.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Espanhol</th>\n",
       "      <td>3</td>\n",
       "      <td>53.87</td>\n",
       "      <td>0</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paraguaio</th>\n",
       "      <td>3</td>\n",
       "      <td>47.33</td>\n",
       "      <td>0</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>8</td>\n",
       "      <td>45.76</td>\n",
       "      <td>8</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sérvio</th>\n",
       "      <td>2</td>\n",
       "      <td>27.30</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguaio</th>\n",
       "      <td>10</td>\n",
       "      <td>53.79</td>\n",
       "      <td>1</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuelano</th>\n",
       "      <td>1</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Quantidade Aproveitamento médio Títulos Médio tempo no comando\n",
       "Nacionalidade                                                                \n",
       "Alemão                  1                83.00       0                   2.00\n",
       "Argentino              10                49.24       3                   6.80\n",
       "Chileno                 1                60.70       0                   7.00\n",
       "Colombiano              1                51.00       0                   4.00\n",
       "Espanhol                3                53.87       0                   3.33\n",
       "Paraguaio               3                47.33       0                   7.67\n",
       "Português               8                45.76       8                   6.25\n",
       "Sérvio                  2                27.30       0                   2.00\n",
       "Uruguaio               10                53.79       1                   4.70\n",
       "Venezuelano             1                53.00       0                   2.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_purchasing_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3415b8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
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