subs_br21.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b5e79ce-bb84-48e4-a380-a3a95cb3046a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\willi\\anaconda3\\lib\\site-packages\\soupsieve\\css_parser.py:813: FutureWarning: The pseudo class ':contains' is deprecated, ':-soup-contains' should be used moving forward.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import soup\n",
    "import pandas as pd\n",
    "\n",
    "dfs_final = soup.dfs_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f091fa68-f8aa-4952-94de-f907c9e159df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pré-processamento do dataframe\n",
    "\n",
    "cols = soup.cols\n",
    "brdf = dfs_final.reset_index(drop=True).rename(**cols)\n",
    "\n",
    "brdf.loc[:, 'time_casa':'tecnico_fora'] = brdf.loc[:, 'time_casa':'tecnico_fora'].astype('str').replace(['dict_values\\(\\[\\'', \"\\']\\)\", \"\\(\\)\",], '', regex=True)\n",
    "\n",
    "brdf.loc[:, 'gols_casa':'gols_fora'] = brdf.loc[:, 'gols_casa':'gols_fora'].astype('str').replace(['dict_values\\(\\[\\'', \"\\']\\)\", \"\\(\\)\"], '', regex=True).astype('int')\n",
    "\n",
    "brdf.loc[:, '11_casa':'sub1_casa'] = brdf.loc[:, '11_casa':'sub1_casa'].astype('str').replace(['dict_values\\(\\[\\\\[\\'', \"\\']\\]\\)\", \"\\(\\)\", \"\\'\"], '', regex=True).astype('str')\n",
    "\n",
    "try:\n",
    "    brdf.loc[:, ['sub1_casa_tempo','sub2_casa_tempo', 'sub3_casa_tempo', \n",
    "                 'sub4_casa_tempo', 'sub5_casa_tempo', 'sub1_fora_tempo',\n",
    "                 'sub2_fora_tempo', 'sub3_fora_tempo', 'sub4_fora_tempo', 'sub5_fora_tempo']] = brdf.loc[:, ['sub1_casa_tempo','sub2_casa_tempo', 'sub3_casa_tempo', \n",
    "                 'sub4_casa_tempo', 'sub5_casa_tempo', 'sub1_fora_tempo',\n",
    "                 'sub2_fora_tempo', 'sub3_fora_tempo', 'sub4_fora_tempo', 'sub5_fora_tempo']].astype('str').astype('int')\n",
    "except AttributeError:\n",
    "    None\n",
    "except ValueError:\n",
    "    None\n",
    "    \n",
    "    \n",
    "brdf.loc[:, ['sub1_casa','sub2_casa', 'sub3_casa', 'sub4_casa', 'sub5_casa',\n",
    "             'sub1_fora','sub2_fora', 'sub3_fora', 'sub4_fora', 'sub5_fora']] = brdf.loc[:, ['sub1_casa','sub2_casa', \n",
    "                                                                                             'sub3_casa', 'sub4_casa', 'sub5_casa', \n",
    "                                                                                             'sub1_fora','sub2_fora', 'sub3_fora', \n",
    "                                                                                             'sub4_fora', 'sub5_fora']].astype('str').replace(['dict_values\\(\\[\\(',\n",
    "                                                                                                                                               \"\\)]\\)\", \"\\(\\)\", \"\\'\"], '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be7fb09c-06e1-46f1-9523-110c849af028",
   "metadata": {},
   "outputs": [],
   "source": [
    "brdf.loc[brdf.tecnico_casa == 'Marcelo Cabo', 'time_casa'] = 'Atlético Goianiense'\n",
    "brdf.loc[brdf.tecnico_casa == 'Eduardo Barroca', 'time_casa'] = 'Atlético Goianiense'\n",
    "brdf.loc[brdf.tecnico_fora == 'Marcelo Cabo', 'time_fora'] = 'Atlético Goianiense'\n",
    "brdf.loc[brdf.tecnico_fora == 'Eduardo Barroca', 'time_fora'] = 'Atlético Goianiense'\n",
    "brdf.loc[brdf.tecnico_fora == 'Eduardo Souza', 'time_fora'] = 'Atlético Goianiense'\n",
    "\n",
    "brdf.loc[brdf.time_casa == 'Atl', 'time_casa'] = 'Athletico Paranaense'\n",
    "brdf.loc[brdf.time_fora == 'Atl', 'time_fora'] = 'Athletico Paranaense'\n",
    "\n",
    "brdf.loc[(brdf['tecnico_fora'] == 'dummy') & (brdf['time_fora'] == 'Athletico Paranaense'), 'tecnico_fora'] = 'James Freitas'\n",
    "brdf.loc[(brdf['tecnico_fora'] == 'dummy') & (brdf['time_fora'] == 'Juventude'), 'tecnico_fora'] = 'Emerson Ávila'\n",
    "\n",
    "brdf.loc[brdf.tecnico_fora == 'Capitão: Cássio', 'tecnico_fora'] = 'Sylvinho'\n",
    "brdf.loc[brdf.tecnico_fora == 'Thiago Mendes', 'tecnico_fora'] = 'Sylvinho'\n",
    "brdf.loc[brdf.tecnico_casa == 'Thiago Mendes', 'tecnico_casa'] = 'Sylvinho'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f63dbbe7-9103-497f-96e3-a8779aaab036",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub1_casa = brdf.loc[:, ['time_casa', 'tecnico_casa', 'sub1_casa', 'sub1_casa_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub2_casa = brdf.loc[:, ['time_casa', 'tecnico_casa', 'sub2_casa', 'sub2_casa_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub3_casa = brdf.loc[:, ['time_casa', 'tecnico_casa', 'sub3_casa', 'sub3_casa_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub4_casa = brdf.loc[:, ['time_casa', 'tecnico_casa', 'sub4_casa', 'sub4_casa_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub5_casa = brdf.loc[:, ['time_casa', 'tecnico_casa', 'sub5_casa', 'sub5_casa_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "\n",
    "sub1_fora = brdf.loc[:, ['time_fora', 'tecnico_fora', 'sub1_fora', 'sub1_fora_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub2_fora = brdf.loc[:, ['time_fora', 'tecnico_fora', 'sub2_fora', 'sub2_fora_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub3_fora = brdf.loc[:, ['time_fora', 'tecnico_fora', 'sub3_fora', 'sub3_fora_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub4_fora = brdf.loc[:, ['time_fora', 'tecnico_fora', 'sub4_fora', 'sub4_fora_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n",
    "sub5_fora = brdf.loc[:, ['time_fora', 'tecnico_fora', 'sub5_fora', 'sub5_fora_tempo']].set_axis(['time', 'tecnico', 'sub', 'tempo'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "760d823b-d762-4b77-a7f3-2f7ee42abe56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    Lucas Ramon, João Lucas\n",
       "1                Rodriguinho Marinho, Maycon\n",
       "2                     Igor Gomes, Joao Rojas\n",
       "3                    Allan, Federico Zaracho\n",
       "4       João Ricardo Riedi, Vinícius Machado\n",
       "                        ...                 \n",
       "2275              Jonathan Cafu, Lucas Ramon\n",
       "2276                 Fábio Gonçalves, Marlon\n",
       "2277                Juan Santos, Vitor Bueno\n",
       "2278          André Inácio, Thiago Fernandes\n",
       "2279                 Renzo Saravia, Mauricio\n",
       "Name: sub, Length: 2280, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs_all = pd.concat([sub1_casa, sub2_casa, sub3_casa, sub4_casa, sub5_casa, sub1_fora], ignore_index=True)\n",
    "\n",
    "subs_all['sub'].replace(['dict_values([])', '[]', r'dict_values([])'], '')\n",
    "# subs_all.loc[subs_all.tecnico_fora == 'dict_values\\(\\[\\]\\)', 'sub'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bebdd6f-0bd7-4690-b3b8-f8b5385ba334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.82240306366683"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statistics\n",
    "\n",
    "tempo_li = subs_all.loc[subs_all['tempo'].astype(str).str.isdigit(), 'tempo'].reset_index(drop=True)\n",
    "statistics.mean(tempo_li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a7de138-f0c3-4031-9451-becd6f17c11d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Contagem')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(rc={\"figure.dpi\":300, 'savefig.dpi':300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "hist = sns.histplot(data=tempo_li, label=\"tempo\", kde=False)\n",
    "hist.set_xlim(0,90)\n",
    "hist.set_xlabel(\"Tempo\", fontsize = 12)\n",
    "hist.set_ylabel(\"Contagem\", fontsize = 12)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a64ab734-1cc0-438c-a64f-24bb5ea1b36b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    Lucas Ramon, João Lucas\n",
       "1                Rodriguinho Marinho, Maycon\n",
       "2                     Igor Gomes, Joao Rojas\n",
       "3                    Allan, Federico Zaracho\n",
       "4       João Ricardo Riedi, Vinícius Machado\n",
       "                        ...                 \n",
       "2275              Jonathan Cafu, Lucas Ramon\n",
       "2276                 Fábio Gonçalves, Marlon\n",
       "2277                Juan Santos, Vitor Bueno\n",
       "2278          André Inácio, Thiago Fernandes\n",
       "2279                 Renzo Saravia, Mauricio\n",
       "Name: sub, Length: 2086, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs_all = subs_all[subs_all['sub'] != r'dict_values([])']\n",
    "\n",
    "subs_all['sub']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8ec13f3-1f57-4671-9918-b5ef6893a572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Vitinho              20\n",
       " Marquinhos           19\n",
       " Chico                19\n",
       " Lucas Melão Lucão    17\n",
       " Romarinho            16\n",
       "                      ..\n",
       " Pierre                1\n",
       " João Pedro Pepê       1\n",
       " Ricardo Silva         1\n",
       " Felipe Augusto        1\n",
       " Nicolás Castillo      1\n",
       "Name: sub_entrou, Length: 496, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs_all['sub_entrou'] = subs_all['sub'].str.split(',').str[1]\n",
    "subs_all['sub_saiu'] = subs_all['sub'].str.split(',').str[0]\n",
    "\n",
    "subs_all['sub_entrou'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c92cb038-cf98-4e88-baf4-56fcca9d1287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marquinhos Santos                 20\n",
       "Maurício Barbieri                 19\n",
       "Cuca                              19\n",
       "Abel Ferreira                     19\n",
       "Sylvinho                          19\n",
       "Juan Pablo Vojvoda                19\n",
       "Diego Aguirre                     17\n",
       "Vágner Mancini                    17\n",
       "Guto Ferreira                     16\n",
       "Eduardo Barroca                   15\n",
       "Jorginho                          15\n",
       "Renato Gaúcho                     14\n",
       "Tiago Nunes                       13\n",
       "Hernán Crespo                     13\n",
       "Jair Ventura                      11\n",
       "Gustavo Florentín                 10\n",
       "António Oliveira                  10\n",
       "Marcão                            10\n",
       "Fábio Carille                     10\n",
       "Umberto Lourenço Louzer Filho      9\n",
       "Roger Machado                      9\n",
       "Rogério Ceni                       9\n",
       "Fernando Diniz                     8\n",
       "Alberto Valentim                   7\n",
       "Dado Cavalcanti                    7\n",
       "Pintado                            7\n",
       "Luiz Felipe Scolari                7\n",
       "Felipe Endres                      5\n",
       "Luiz Fernando Iubel                4\n",
       "Diego Dabove                       4\n",
       "Marcelo Cabo                       3\n",
       "Thiago Gomes Pacheco               2\n",
       "Lisca                              2\n",
       "Maurício Ferreira De Souza         2\n",
       "Bruno Lopes                        1\n",
       "Eduardo Souza                      1\n",
       "Emerson Ávila                      1\n",
       "Miguel Ángel Ramírez               1\n",
       "Cauan                              1\n",
       "James Freitas                      1\n",
       "Osmar Loss Vieira                  1\n",
       "Eduardo Zuma                       1\n",
       "Paulo Autuori                      1\n",
       "Name: tecnico_fora, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brdf['tecnico_fora'].value_counts()\n",
    "\n",
    "# brdf.loc[brdf['tecnico_fora'] == 'Capitão: Cássio']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "364c012c-a9ca-4761-9afd-fd98a5fcb2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "subs_tec = subs_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "subs_tec.to_csv('subs_tec.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc0a874b-050d-45a7-9504-d5b8901a3893",
   "metadata": {},
   "outputs": [],
   "source": [
    "# u/thedragongamer\n",
    "\n",
    "sub1_all = pd.concat([sub1_casa, sub1_fora], ignore_index=True)\n",
    "sub2_all = pd.concat([sub2_casa, sub2_fora], ignore_index=True)\n",
    "sub3_all = pd.concat([sub3_casa, sub3_fora], ignore_index=True)\n",
    "sub4_all = pd.concat([sub4_casa, sub4_fora], ignore_index=True)\n",
    "sub5_all = pd.concat([sub5_casa, sub5_fora], ignore_index=True)\n",
    "\n",
    "\n",
    "subs1_tec = sub1_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "subs1_tec.to_csv('subs1_tec.csv')\n",
    "\n",
    "subs2_tec = sub2_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "subs2_tec.to_csv('subs2_tec.csv')\n",
    "\n",
    "subs3_tec = sub3_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "subs3_tec.to_csv('subs3_tec.csv')\n",
    "\n",
    "sub4_tec = sub4_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "sub4_tec.to_csv('subs4_tec.csv')\n",
    "\n",
    "sub5_tec = sub5_all.groupby('tecnico').apply(lambda df: df.tempo.astype(str).iloc[:])\n",
    "sub5_tec.to_csv('subs5_tec.csv')"
   ]
  }
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