latestresults.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 215,
   "source": [
    "import re\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from matplotlib.colors import Normalize, PowerNorm\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Результаты паракрана"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Здесь можно подставить идентификатор любого другого паркрана."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "source": [
    "parkrun = 'yoshkarolaalleyazdorovya'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Список идентификаторов всех российских паркранов находится в файле `russian_parkruns.csv`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Какие результаты получить - последние или же по конкретному номеру (закомментировать одну из строк с `suff`):"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "source": [
    "suff = '/latestresults/'  # результаты последнего паркрана, внесённого в систему"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "source": [
    "N = 100  # либо указываем номер и раскомментируем следующую строчку"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "source": [
    "# suff = f'/weeklyresults/?runSeqNumber={N}'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "source": [
    "url = f'https://www.parkrun.ru/{parkrun}/results' + suff\n",
    "page_all_results = requests.get(url, headers={'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:86.0) Gecko/20100101 Firefox/86.0'})"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "source": [
    "data = pd.read_html(page_all_results.text)[0]\n",
    "unknown_runners = len(data[data['Участник'] == 'Неизвестный'])\n",
    "number_runners = len(data)\n",
    "data.dropna(thresh=4, inplace=True, axis=0)\n",
    "data.reset_index(drop=True, inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "source": [
    "# Общее количество участников забега\n",
    "number_runners"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "metadata": {},
     "execution_count": 223
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "source": [
    "# Количество \"неизвестных\" участников в данном забеге\n",
    "unknown_runners"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 224
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "source": [
    "data['Позиция м/ж'] = data[data.columns[2]].dropna().transform(lambda s: int(re.search(r'(?:Мужской|Женский)[ ]+(\\d+)', s)[1]))\n",
    "data[data.columns[2]] = data[data.columns[2]].dropna().transform(lambda s: re.search(r'Мужской|Женский', s)[0])\n",
    "\n",
    "data[data.columns[1]] = data[data.columns[1]].transform(lambda s: re.search(r'([^\\d]+)\\d.*|Неизвестный', s)[1])\n",
    "\n",
    "data[data.columns[3]] = data[data.columns[3]].dropna().transform(lambda s: re.search(r'\\w\\w\\d\\d-\\d\\d|\\w\\w10|\\w\\w---', s)[0])\n",
    "\n",
    "data['Личник'] = data[data.columns[5]].dropna().transform(lambda s: re.search(r'(?<=\\d\\d:\\d\\d)(.*)', s)[1])\n",
    "data[data.columns[5]] = data[data.columns[5]].dropna().transform(lambda s: re.search(r'^(\\d:)?\\d\\d:\\d\\d', s)[0])\n",
    "\n",
    "#data['Клуб'].fillna('',inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "source": [
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Позиция              Участник      Пол Возр. группа       Клуб  Время  \\\n",
       "0         1       Алексей МАТВЕЕВ  Мужской      ВМ40-44        NaN  17:18   \n",
       "1         2        Леонид СУВОРОВ  Мужской      ВМ35-39     Айвика  17:24   \n",
       "2         4     Александр РЫБАКОВ  Мужской      ВМ35-39     Айвика  20:33   \n",
       "3         5    Евгений ВЕТЛУЖСКИХ  Мужской      ВМ40-44        NaN  20:37   \n",
       "4         6           Peter ZDRAV  Мужской      СМ30-34        NaN  21:04   \n",
       "5         7        Богдан ИСАЕНКО  Мужской      СМ18-19        NaN  21:08   \n",
       "6         9    Сергей ПРОСВЕРИКОВ  Мужской      ВМ35-39        NaN  24:35   \n",
       "7        10    Константин СМИРНОВ  Мужской      ВМ40-44        NaN  25:10   \n",
       "8        11   Константин САМСОНОВ  Мужской      СМ30-34        NaN  25:53   \n",
       "9        12      Владимир ШИГАЛЕВ  Мужской      ВМ65-69        NaN  26:52   \n",
       "10       14    Людмила БАСТРАКОВА  Женский      ВЖ45-49        NaN  28:06   \n",
       "11       15        Руслан БАБУРОВ  Мужской      ВМ45-49  БК Митино  29:25   \n",
       "12       16     Марина ВЕТЛУЖСКИХ  Женский      СЖ30-34        NaN  29:41   \n",
       "13       17           Иван ЗЕМЦОВ  Мужской      ВМ50-54     Айвика  29:46   \n",
       "14       18  Александра САВЕЛЬЕВА  Женский      СЖ30-34        NaN  30:01   \n",
       "15       19         Тимур НЕФЁДОВ  Мужской      ЮМ11-14  БК Митино  31:30   \n",
       "16       20    Варвара ВЕТЛУЖСКИХ  Женский      ЮЖ11-14        NaN  33:01   \n",
       "17       21     Радик ШАМСУТДИНОВ  Мужской      ВМ40-44        NaN  33:10   \n",
       "18       22      Надежда БАБУРОВА  Женский      ВЖ35-39        NaN  33:46   \n",
       "19       23          Вера ЗАМКОВА  Женский      ВЖ60-64        NaN  36:43   \n",
       "20       24    Екатерина МОРОЗОВА  Женский      ЮЖ11-14        NaN  39:28   \n",
       "21       25      Валерий ПЛАТОНОВ  Мужской      ВМ60-64        NaN  39:34   \n",
       "\n",
       "    Позиция м/ж           Личник  \n",
       "0             1         ЛР 17:16  \n",
       "1             2         ЛР 17:24  \n",
       "2             3         ЛР 19:54  \n",
       "3             4         ЛР 18:20  \n",
       "4             5         ЛР 20:11  \n",
       "5             6         ЛР 21:06  \n",
       "6             7         ЛР 23:17  \n",
       "7             8         ЛР 24:38  \n",
       "8             9         ЛР 24:04  \n",
       "9            10         ЛР 25:59  \n",
       "10            1         ЛР 27:24  \n",
       "11           11  Первый паркран!  \n",
       "12            2         ЛР 27:50  \n",
       "13           12         ЛР 24:35  \n",
       "14            3  Первый паркран!  \n",
       "15           13  Первый паркран!  \n",
       "16            4         ЛР 32:14  \n",
       "17           14         ЛР 32:44  \n",
       "18            5  Первый паркран!  \n",
       "19            6         ЛР 32:22  \n",
       "20            7         ЛР 37:59  \n",
       "21           15         ЛР 24:07  "
      ],
      "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>Позиция</th>\n",
       "      <th>Участник</th>\n",
       "      <th>Пол</th>\n",
       "      <th>Возр. группа</th>\n",
       "      <th>Клуб</th>\n",
       "      <th>Время</th>\n",
       "      <th>Позиция м/ж</th>\n",
       "      <th>Личник</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Алексей МАТВЕЕВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ40-44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17:18</td>\n",
       "      <td>1</td>\n",
       "      <td>ЛР 17:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Леонид СУВОРОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ35-39</td>\n",
       "      <td>Айвика</td>\n",
       "      <td>17:24</td>\n",
       "      <td>2</td>\n",
       "      <td>ЛР 17:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Александр РЫБАКОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ35-39</td>\n",
       "      <td>Айвика</td>\n",
       "      <td>20:33</td>\n",
       "      <td>3</td>\n",
       "      <td>ЛР 19:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Евгений ВЕТЛУЖСКИХ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ40-44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20:37</td>\n",
       "      <td>4</td>\n",
       "      <td>ЛР 18:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Peter ZDRAV</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>СМ30-34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:04</td>\n",
       "      <td>5</td>\n",
       "      <td>ЛР 20:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Богдан ИСАЕНКО</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>СМ18-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21:08</td>\n",
       "      <td>6</td>\n",
       "      <td>ЛР 21:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Сергей ПРОСВЕРИКОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ35-39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24:35</td>\n",
       "      <td>7</td>\n",
       "      <td>ЛР 23:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>Константин СМИРНОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ40-44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25:10</td>\n",
       "      <td>8</td>\n",
       "      <td>ЛР 24:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>Константин САМСОНОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>СМ30-34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25:53</td>\n",
       "      <td>9</td>\n",
       "      <td>ЛР 24:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>Владимир ШИГАЛЕВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ65-69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26:52</td>\n",
       "      <td>10</td>\n",
       "      <td>ЛР 25:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>Людмила БАСТРАКОВА</td>\n",
       "      <td>Женский</td>\n",
       "      <td>ВЖ45-49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28:06</td>\n",
       "      <td>1</td>\n",
       "      <td>ЛР 27:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>Руслан БАБУРОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ45-49</td>\n",
       "      <td>БК Митино</td>\n",
       "      <td>29:25</td>\n",
       "      <td>11</td>\n",
       "      <td>Первый паркран!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>Марина ВЕТЛУЖСКИХ</td>\n",
       "      <td>Женский</td>\n",
       "      <td>СЖ30-34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29:41</td>\n",
       "      <td>2</td>\n",
       "      <td>ЛР 27:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>Иван ЗЕМЦОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ50-54</td>\n",
       "      <td>Айвика</td>\n",
       "      <td>29:46</td>\n",
       "      <td>12</td>\n",
       "      <td>ЛР 24:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>Александра САВЕЛЬЕВА</td>\n",
       "      <td>Женский</td>\n",
       "      <td>СЖ30-34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30:01</td>\n",
       "      <td>3</td>\n",
       "      <td>Первый паркран!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>Тимур НЕФЁДОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ЮМ11-14</td>\n",
       "      <td>БК Митино</td>\n",
       "      <td>31:30</td>\n",
       "      <td>13</td>\n",
       "      <td>Первый паркран!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>Варвара ВЕТЛУЖСКИХ</td>\n",
       "      <td>Женский</td>\n",
       "      <td>ЮЖ11-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33:01</td>\n",
       "      <td>4</td>\n",
       "      <td>ЛР 32:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21</td>\n",
       "      <td>Радик ШАМСУТДИНОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ40-44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33:10</td>\n",
       "      <td>14</td>\n",
       "      <td>ЛР 32:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>22</td>\n",
       "      <td>Надежда БАБУРОВА</td>\n",
       "      <td>Женский</td>\n",
       "      <td>ВЖ35-39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33:46</td>\n",
       "      <td>5</td>\n",
       "      <td>Первый паркран!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23</td>\n",
       "      <td>Вера ЗАМКОВА</td>\n",
       "      <td>Женский</td>\n",
       "      <td>ВЖ60-64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36:43</td>\n",
       "      <td>6</td>\n",
       "      <td>ЛР 32:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>Екатерина МОРОЗОВА</td>\n",
       "      <td>Женский</td>\n",
       "      <td>ЮЖ11-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39:28</td>\n",
       "      <td>7</td>\n",
       "      <td>ЛР 37:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>Валерий ПЛАТОНОВ</td>\n",
       "      <td>Мужской</td>\n",
       "      <td>ВМ60-64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39:34</td>\n",
       "      <td>15</td>\n",
       "      <td>ЛР 24:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 226
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "source": [
    "data['result_m'] = data['Время'].transform(lambda time: sum(x * int(t) for x, t in zip([1/60, 1, 60], time.split(':')[::-1])))\n",
    "data.info()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22 entries, 0 to 21\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Позиция       22 non-null     int64  \n",
      " 1   Участник      22 non-null     object \n",
      " 2   Пол           22 non-null     object \n",
      " 3   Возр. группа  22 non-null     object \n",
      " 4   Клуб          5 non-null      object \n",
      " 5   Время         22 non-null     object \n",
      " 6   Позиция м/ж   22 non-null     int64  \n",
      " 7   Личник        22 non-null     object \n",
      " 8   result_m      22 non-null     float64\n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "source": [
    "data.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Позиция          0\n",
       "Участник         0\n",
       "Пол              0\n",
       "Возр. группа     0\n",
       "Клуб            17\n",
       "Время            0\n",
       "Позиция м/ж      0\n",
       "Личник           0\n",
       "result_m         0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 228
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "source": [
    "data['result_m'].describe()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "count    22.000000\n",
       "mean     27.943182\n",
       "std       6.576076\n",
       "min      17.300000\n",
       "25%      21.995833\n",
       "50%      28.758333\n",
       "75%      32.637500\n",
       "max      39.566667\n",
       "Name: result_m, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 229
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Топ3 мужчины и женщины"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "source": [
    "data[data['Позиция м/ж'] < 4].drop(data.iloc[:,[0,2,3,4,7,8]], axis=1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                Участник  Время  Позиция м/ж\n",
       "0        Алексей МАТВЕЕВ  17:18            1\n",
       "1         Леонид СУВОРОВ  17:24            2\n",
       "2      Александр РЫБАКОВ  20:33            3\n",
       "10    Людмила БАСТРАКОВА  28:06            1\n",
       "12     Марина ВЕТЛУЖСКИХ  29:41            2\n",
       "14  Александра САВЕЛЬЕВА  30:01            3"
      ],
      "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>Участник</th>\n",
       "      <th>Время</th>\n",
       "      <th>Позиция м/ж</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Алексей МАТВЕЕВ</td>\n",
       "      <td>17:18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Леонид СУВОРОВ</td>\n",
       "      <td>17:24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Александр РЫБАКОВ</td>\n",
       "      <td>20:33</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Людмила БАСТРАКОВА</td>\n",
       "      <td>28:06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Марина ВЕТЛУЖСКИХ</td>\n",
       "      <td>29:41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Александра САВЕЛЬЕВА</td>\n",
       "      <td>30:01</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 230
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Медианное время"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "source": [
    "data['result_m'].median()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "28.758333333333333"
      ]
     },
     "metadata": {},
     "execution_count": 231
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Квантиль"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "source": [
    "q = data['result_m'].quantile(q=0.95)\n",
    "q"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "39.329166666666666"
      ]
     },
     "metadata": {},
     "execution_count": 232
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Распределение участников по полу"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "source": [
    "sns.set_style('whitegrid')\n",
    "ax = sns.countplot(x='Пол', data=data)\n",
    "for p, label in zip(ax.patches, data['Пол'].value_counts().values):\n",
    "    ax.annotate(label, (p.get_x()+0.375, p.get_height()+0.15))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Количество участников из беговых клубов"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "source": [
    "clubs = data['Клуб'].value_counts()\n",
    "plt.figure(figsize=(16, 7))\n",
    "plt.xticks(rotation=70)\n",
    "sns.set_style('whitegrid')\n",
    "plt.yticks(list(range(0, clubs[0], 2)))\n",
    "ax = sns.countplot(x='Клуб', data=data, palette='husl', order=clubs.index)\n",
    "for p, label in zip(ax.patches, clubs.values):\n",
    "    ax.annotate(label, (p.get_x()+0.375, p.get_height()+0.15))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6YAAAHhCAYAAAB9QKMWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAgbUlEQVR4nO3da5TU9WH/8c+yC4hAxBAlFAOCICIgajlq1DZIBEmVAKImp8ZDvCW2VWJrtV4a2xqjpj2aa3usSfMnWiu1jUoMwaSiIoRovCFgwUtDVRAXAwKCXJbZ+T9o4WhM7JowfLM7r9ej3dnLfHji+N7fZRqq1Wo1AAAAUEin0gMAAACob8IUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKKqp9ACgNrZt25Yzzzwz27dvT6VSyUknnZTp06eXngUAAO/Q4H1MoWOqVqt58803071797S0tOQP//APc9VVV+Xwww8vPQ0AAN7GqbzQQTU0NKR79+5Jkh07dmTHjh1paGgovAoAAN5JmEIHVqlUMmnSpBx77LE59thjM2rUqNKTAADgHYQpdGCNjY2ZNWtW5s2bl8WLF+e5554rPQkAAN5BmEIdeN/73pejjz468+fPLz0FAADeQZhCB7Vu3bps3LgxSbJ169YsXLgwgwYNKrwKAADeydvFQAe1Zs2aXH755alUKqlWq5kwYUJOOOGE0rMAAOAdfqveLmbRokXp2rVr6RkAAADsZtu2bfuVb134W3XEtGvXrhk2bFjpGQAAAOxmy5Yt+5Vfc40pAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihOlvoLpjR+kJAPwv/00GgParqfSA9qyhqSmrr/lG6RkAJOl79YWlJwAAvyZHTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKaSg8AAIDdYfXq1bnsssuydu3aNDQ05Iwzzsi0adNKzwLaQJgCANAhNDY25vLLL8/w4cOzadOmTJ06Nccdd1wGDx5cehrwf3AqLwAAHcL++++f4cOHJ0l69OiRQYMGpbm5ufAqoC2EKQAAHc7KlSuzbNmyjBo1qvQUoA2EKQAAHcrmzZszffr0XHnllenRo0fpOUAbCFMAADqMlpaWTJ8+PRMnTsz48eNLzwHaSJgCANAhVKvVXHXVVRk0aFDOPvvs0nOA90CYAgDQITzxxBOZNWtWHnnkkUyaNCmTJk3KvHnzSs8C2sDbxQAA0CGMHj06zz77bOkZwK/BEVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoKimWv3i1atX57LLLsvatWvT0NCQM844I9OmTavV0wEAANBO1SxMGxsbc/nll2f48OHZtGlTpk6dmuOOOy6DBw+u1VMCAADQDtXsVN79998/w4cPT5L06NEjgwYNSnNzc62eDgAAgHZqj1xjunLlyixbtiyjRo3aE08HAABAO1KzU3l32rx5c6ZPn54rr7wyPXr0eNfv3bZtW5YtW1brSbvNsGHDSk8A4C3a02tIezVg0MDs3XWv0jMASPLmtq158WcrSs/YLWoapi0tLZk+fXomTpyY8ePH/5/f37VrV7EHwK/Na8ieMf7/3Vx6AgBJfnT2Be3qte/d/oBcs1N5q9VqrrrqqgwaNChnn312rZ4GAACAdq5mYfrEE09k1qxZeeSRRzJp0qRMmjQp8+bNq9XTAQAA0E7V7FTe0aNH59lnn63VrwcAAKCD2CN35QUAAIBfRZgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoKg2hem0adPa9BgAAAC8V03v9sVt27Zly5Ytef3117Nhw4ZUq9UkyaZNm9Lc3LxHBgIAANCxvWuYzpw5M9/5zneyZs2anHrqqbvCtEePHvnUpz61RwYCAADQsb1rmE6bNi3Tpk3LbbfdlrPOOmtPbQIAAKCOvGuY7nTWWWflySefzKpVq1KpVHY9Pnny5FrtAgAAoE60KUwvvfTSvPzyyznkkEPS2NiYJGloaBCmAAAA/MbaFKZLly7ND37wgzQ0NNR6DwAAAHWmTW8XM2TIkLz22mu13gIAAEAdatMR09dffz0nn3xyDjvssHTu3HnX4zfffHPNhgEAAFAf2hSmF110Ua13AAAAUKfaFKZHHXVUrXcAAABQp9oUpkccccSuGx+1tLRkx44d6datW5588smajgMAAKDja1OYPvXUU7s+rlarmTt3bhYtWlSrTQAAANSRNt2V960aGhpy4oknZsGCBbXYAwAAQJ1p0xHTH/3oR7s+bm1tzdKlS9O1a9eajQIAAKB+tClMH3zwwV0fNzY2pl+/fvmHf/iHmo0CAACgfrQpTK+//vpa7wAAAKBOteka01dffTV/8id/kg9/+MP58Ic/nIsuuiivvvpqrbcBAABQB9oUpldccUXGjh2b+fPnZ/78+TnhhBNyxRVX1HobAAAAdaBNYbpu3bpMnTo1TU1NaWpqyqmnnpp169bVehsAAAB1oE1h2qtXr8yaNSuVSiWVSiWzZs1Kr169ajwNAACAetCmML3uuusyZ86cHHfccTn++OPzwx/+MDfccEOttwEAAFAH2nRX3q997Wv50pe+lH322SdJsn79+nzpS19yt14AAAB+Y206Yvrss8/uitLkf07tXbZsWc1GAQAAUD/aFKatra3ZsGHDrs/Xr1+fSqVSs1EAAADUjzadynvOOefkE5/4RCZMmJAkue+++3LBBRfUdBgAAAD1oU1hOnny5IwYMSKPPPJIkuQb3/hGBg8eXNNhAAAA1Ic2hWmSDB48WIwCAACw27XpGlMAAACoFWEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUcIUAACAooQpAAAARQlTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFHCFAAAgKKEKQAAAEUJUwAAAIoSpgAAABQlTAEAAChKmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkAAABFCVMAAACKEqYAAAAUJUwBAAAoSpgCAABQlDAFAACgKGEKAABAUTUN04cffjgnnXRSxo0bl1tuuaWWTwUAAEA7VbMwrVQqueaaa/Ktb30rs2fPzve///288MILtXo6AAAA2qmahenixYszYMCAfOhDH0qXLl1y8sknZ+7cubV6OgAAANqpmoVpc3NzPvjBD+76vE+fPmlubq7V0wEAANBONZUe8Fbbtm3LsmXLSs94b07/aOkFACRZ395eP9qxrx7zkdITAEjaXTtt27btV36tZmHap0+fvPrqq7s+b25uTp8+fd71Zw4//PBazQEAAOC3VM1O5R05cmT++7//Oy+//HK2b9+e2bNnZ+zYsbV6OgAAANqpmh0xbWpqytVXX53zzjsvlUolU6dOzZAhQ2r1dAAAALRTDdVqtVp6BAAAAPWrZqfyAgAAQFsIUwAAAIoSpgAAkMQVblCOMAXeplqtemEGoC41NDRk7dq1qVQqpadA3anZXXmB9qmhoWHXx62trenUyd+vAOi4dr7WzZ07N/PmzUtjY2OS5Pd+7/cyZswYr4OwhwhTqHOVSiWNjY15/vnn8+CDD+aVV17J1KlTM3LkSC/GAHR4nTp1SrVazde//vV86lOfyj777JPNmzdn1qxZ+cAHPpDDDjus9ESoC8IU6tzOvwxfcskl+bM/+7PMmDEjs2bNSo8ePfKxj30s06dPT/fu3d92JBUAOpIf//jH2W+//XLaaaclSbZv355NmzZl5syZGT58+K7XSqB2HA6BOrbzWtJHH300BxxwQI4++ugceOCBeeqppzJ+/PjMmTMnmzdvFqUAdGgDBw7M5s2bc/vtt+f1119Ply5d0qdPn2zcuFGUwh7iiCnUsZ3B+fLLL+eUU07JwoUL07t37yTJCSeckD59+qRPnz4lJwJAzfXr1y8XXHBB5s6dm2XLlmXFihXp2bNnzjzzzNLToG4IUyCnnXZaNm/enFdffTU9e/bMnXfemZkzZ2bq1KmlpwFATey86dHTTz+dn//85xkxYkR69eqVzZs3Z8OGDTn00EPTv3//0jOhbjRUvS8E1KW3viDvvffeGTJkSKrVau6888688MILGThwYE477bR06dKl9FQAqJkZM2ZkwYIFaWpqyvHHH5/hw4dn8ODB6dmzZ+lpUFeEKdS5GTNmZPv27fnMZz6z67FNmzalR48eBVcBwJ6zbt26fOxjH8vYsWOzfv36dOvWLZ///Oez7777lp4GdcOpvFDnTj311Fx44YUZPXp0li5dmjfeeCNz587Nddddl0MOOaT0PADY7arVahoaGvLiiy9mwIABWbhwYSZOnJi//Mu/zKpVq7Jo0SJRCnuYI6ZQh3aexrt48eJs2bIlCxYsyE9+8pMceeSROeyww3LggQdmxIgRpWcCQE19+ctfTpcuXfKDH/wgp59+ej796U+XngR1yxFTqEOdOv3PO0UtWbIkS5YsSUtLS9avX58JEybkyCOPTJJUKhW3yAegQzv++OPzzDPP5JBDDsmqVatyyy235Hd+53cyYcKENDX532TYkxwxhTqz8/SlnV544YUsWbIk8+fPT2NjY7p165aLL74473//+wuuBIDa2PmH14ceeih9+/bN0KFDs3Xr1jz++ON59NFH06tXr5x77rmlZ0LdEaZQZ3a+IN96661pbm7O5MmTM2TIkCTJU089lZdeeimTJk0qvBIAamvChAnZvn17hg0bllGjRmX8+PE58MADs3Xr1uy1116l50HdEaZQp6688srcddddGTp0aLp06ZKxY8fm5JNP9p5tAHRoO/9A+2//9m+ZP39+pkyZkuXLl+epp55Kz54987d/+7cuZYEChCnUmZ2n8r722mu5/fbbc8QRR6RXr1757ne/m0cffTQzZ850J0IAOqRfvJzl5ptvzqJFi3LxxRenc+fO+fnPf56jjz664EKoX8IU6sjOvxKvXr06ffv2zfLly3PDDTfkjDPOyB/8wR94/1IAOrw33ngj48aNyyc+8Ymcf/75WbRoUdatW5dTTjll180BgT1PmEKdqVarOfnkk9O9e/ecddZZ6d+/f2bPnp1TTjklo0aNKj0PAGrirUdLn3jiicyYMSMLFy5M586ds379+lx22WU555xzCq+E+iVMoY689UX5/vvvz8yZM/Ozn/0sr7zySkaNGpV/+Zd/cV0NAB3SztfAiy++OFdfffWuu88/9NBDufnmmzN06ND8zd/8TeGVUL+8QRPUidbW1nTq1CnLli3L5s2bc+KJJ+bEE0/M9u3b88Mf/jAvvviiKAWgQ9r5GrhixYqsXr36bW+JNmbMmIwZMyatra0FFwLCFOpAtVpNp06dsm7dunzuc59L//79M3r06LS0tCRJLrroosILAaB2dp4geM8992TlypX593//9xx++OEZPHjwru9xfSmU5VReqAM7b3r0T//0T9myZUuGDRuWm266Keedd16WLFmSgw8+OJ/85CdLzwSAmvr4xz+eyZMnZ926dXn++efz/ve/PyNHjsyUKVPSrVu30vOgrjliCnVg51+BH3jggUyePDn3339/Pv3pT2fKlCl57rnnsmLFisILAaC2nn322RxwwAE555xzsnbt2jQ3N+eZZ57JY489lgkTJghTKEyYQh1oaGjIjh078pGPfCRz5szJypUr069fv6xYsSKPP/54rrnmmtITAaCmunbtmksuuSRJ0rt37/Tu3TsHHXRQPvrRj77tmlOgDKfyQge3fv369OrVK83Nzdl///3T0NCQJUuW5J577smiRYvSpUuX3HHHHaVnAkDNtLa25q//+q9zzDHH5MADD0zfvn2z7777lp4FvIUwhQ5s+/bt+da3vpU//uM/zuTJkzN48OCMHj06o0aNyrBhw7J169asXbs2/fr1Kz0VAGrmxRdfzJQpUzJq1KiMHDkyPXv2zJAhQ9K/f/8MGjSo9DwgiduPQQd29913Z/ny5UmS4cOH56WXXsrGjRtz11135aabbsp9992Xvn37Fl4JALU1YMCA3HjjjdmwYUOampqybdu2zJw5M48//njpacD/co0pdGCtra0555xzkiTXXHNNLr/88qxatSoTJkzIkiVLsnHjRrfHB6BDqlaraWhoyNatWzN//vz8/u//fvbbb7/853/+ZyZPnpzXX3+99ETgLZzKCx1YS0tLGhsb84UvfCFjxozJEUcckbvvvjtDhw7NMccck82bN6d79+6lZwJAzSxevDjf/va38773vS/77rtvFi5cmKFDh+baa68tPQ14C2EKHdyGDRty2223ZeXKldmwYUPWrVuXtWvX5qabbsphhx1Weh4A1MSDDz6YY445Jt26dcvKlSuzZMmSvPzyy1m9enXuuOOOXHrppTn33HNLzwT+lzCFDmzt2rXp3bt3Wltbs3Tp0rS0tGTFihW59tprM2bMmHzlK18pPREAdruf/OQnOfvsszNkyJAcfvjhOemkk3L88ccnSX72s5/lxz/+cT7ykY+kf//+hZcCOwlT6ICeeeaZXHHFFTn00EPz9NNPZ8SIEendu3d++tOf5kMf+lBWrVqVP/3TP81xxx1XeioA7Hb/9V//la997WtpamrKwIED88wzz2T16tU59thjc8opp+TQQw8tPRH4Be56Ah1QtVrNjh070qdPn9x2220588wz07Nnz1Sr1Zx00kn5oz/6I1EKQId10EEH5brrrsvBBx+cTZs25eKLL84Xv/jFdOrUKRdeeGHuvvvu0hOBX+CIKXRA1Wo1CxYsyA033JDDDz88X/ziF3PppZdmxIgRmTZtWlpbW92NF4AOb/v27bnrrrvy9NNP57zzzstBBx2UTZs2pUuXLunSpUvpecBbCFPowFpaWvKP//iP2bJlS+69997cf//96dKly65b6ANAR/PNb34zPXr0yD777JOlS5dm4MCBmT9/ftasWZMbb7wx/fr1Kz0R+CUcMoEOqrW1NZ07d87HP/7xbNy4MUmyYMGCbNu2TZQC0CGtXbs2N954Y6677ro899xzOeaYY/Laa6/l6KOPzrnnnpu99tqr9ETgV3DEFOrEwoUL853vfCfTp0/P8OHDS88BgN1q59lACxcuzH/8x3/k4YcfzsiRIzNp0qSMHj06PXv2LD0ReBfCFOpEtVrN888/n759+3pxBqDD27JlS+6+++7Mnj07a9euzcCBAzN9+vQMGzas9DTglxCmAAB0GK2tralWq2lsbNz12OrVq3Prrbdm3LhxOfLIIwuuA34VYQoAQIdUqVTSqVMn91aAdsDNjwAAaPceeuihdzzW2NiYhoaGVCqVVCqVPT8KaDNhCgBAu7Zs2bJcf/312bJly67HWltbd33c2Nj4tlN7gd8+whQAgHZt2LBhGTFiRG6++eZdj3Xq1CmbNm3KmjVr8tOf/rTgOqAtmkoPAACA39Sf//mf55JLLsm8efPy0ksvZfHixXnjjTeyatWqfO5znys9D/g/uPkRAADt1uLFi1OpVHLEEUfk7//+7/PP//zP+exnP5tKpZIxY8Zkn332yT777JPOnTuXngq8C0dMAQBot5588snccccdOeCAA7LffvvlAx/4QI466qgceuihpacB74EjpgAAtGtvvPFGHnnkkTzyyCO5++67M2DAgJx77rkZO3Zs9t5779LzgDYQpgAAtEuLFy/Ofvvtl759++56rLm5Offff38eeOCBrFmzJnfddZfTeKEdEKYAALRL559/fv7iL/4igwcPTrVazXPPPZdBgwbtCtFXX301H/zgBwuvBNrCNaYAALQ7zzzzTNavX5/Bgwdn+/bt+fKXv5wFCxZk/fr1Oeqoo/JXf/VXohTaEe9jCgBAu/P9738/p59+epLknnvuycsvv5x77703M2fOzI4dO7JgwYLCC4H3QpgCANDu3HrrrXn88cfT0tKSBQsW5IILLkiS9OvXL/3798+iRYvKDgTeE2EKAEC7c/vtt6dSqeS4447Lj370ozz22GN58803kyTLly/PlClTCi8E3gs3PwIAoF178MEH8+1vfzvLly9Pr1690tTUlDlz5pSeBbwHwhQAgA7hzTffzB133JHevXtn8uTJpecA74EwBQAAoCjXmAIAAFCUMAUAAKAoYQoAAEBRwhQAAICihCkA7CZHHHHEro/XrFmTcePG5YEHHvi1flelUsk3v/nNfPKTn8yUKVNy55137q6ZAPBbp6n0AADoaDZt2pTPfOYzOf/88zN27Nhf63d8/etfT6dOnTJjxozstddeu3khAPx2ccQUAHajlpaWXHTRRRk7dmzOOOOMt31t2LBhmTRpUsaNG5fPfvaz2bRpU8aOHZuWlpYkedvn9957bx5//PGcfvrpmTZtWl555ZUS/xwA2COEKQDsRldeeWUee+yxnHzyyW97vFKppFu3bpk1a1auvfbaJEmPHj1y9NFHZ968eUmS2bNnZ/z48encuXNWrlyZKVOm5N57783EiRN3/QwAdETCFAB2kzfffDPr16/P9ddfn2uuueZtX9u6dWu6du36jp857bTT8t3vfjdJctddd+XUU09NknTq1CmnnHJKkmTSpEl54oknarweAMoRpgCwm3Tp0iVf/epXM3HixDQ1NeV73/verq+tWbMm+++//zt+5nd/93ezatWqPProo6lUKjn44IOTJN27d99juwGgNGEKALtJU1NT9t577yTJ1Vdfna985St54403kiRz5szJkUce+Ut/bvLkybnkkkt2HS1NkpEjR2b27NlJknvvvTejR4+u8XoAKEeYAkANDBgwIKeeempuuumm3HrrrXnyySdz4YUX/tLvnThxYjZu3Ljr1N0k+fznP59//dd/zcSJE/O9730vV1111Z6aDgB7XEO1Wq2WHgEA9ey+++7L3Llz83d/93elpwBAEd7HFAAK+sIXvpCHH344t9xyS+kpAFCMI6YAAAAU5RpTAAAAihKmAAAAFCVMAQAAKEqYAgAAUJQwBQAAoChhCgAAQFH/H6iBhJza86ofAAAAAElFTkSuQmCC"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "source": [
    "clubs = data['Клуб'].value_counts()\n",
    "colors = [('blueviolet' if item == 'Wake&Run' else 'darkkhaki') for item in clubs.index]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "source": [
    "fig = plt.figure(figsize=(16, 7))\n",
    "ax = fig.add_subplot()\n",
    "ax.grid(False, axis='x')\n",
    "ax.yaxis.set_major_locator(MultipleLocator(2))\n",
    "\n",
    "plt.xticks(rotation=70)\n",
    "plt.bar(clubs.index,clubs.values, color=colors)\n",
    "plt.tight_layout()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "source": [
    "norm = PowerNorm(gamma=0.1)\n",
    "colors = plt.cm.tab20b(norm(clubs.values))\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "plt.pie(clubs.values, labels=clubs.index, colors=colors, startangle=80)\n",
    "plt.title('Клубы на паркране Кузьминки', size=15, fontweight='bold')\n",
    "plt.tight_layout()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Распределение по возрастным группам"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "source": [
    "age_groups = data['Возр. группа'].value_counts()\n",
    "plt.figure(figsize=(16, 7))\n",
    "plt.xticks(rotation=70)\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.countplot(data=data, x='Возр. группа', hue='Пол', palette='tab10', order=age_groups.index)\n",
    "print(ax.patches[0])\n",
    "for p, label in zip(ax.patches, age_groups.values):\n",
    "    ax.annotate(label, (p.get_x()+0.1, p.get_height()+0.2))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Rectangle(xy=(-0.4, 0), width=0.4, height=4, angle=0)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Распределение результатов участников"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "source": [
    "plt.figure(figsize=(16, 7))\n",
    "ax = data['result_m'].hist(bins=30)\n",
    "ptchs = ax.patches\n",
    "med = data['result_m'].median()\n",
    "norm = Normalize(0, med)\n",
    "\n",
    "for ptch in ptchs:\n",
    "    ptch_x = ptch.get_x()\n",
    "    color = plt.cm.viridis(norm(med - abs(med - ptch_x)))\n",
    "    ptch.set_facecolor(color)\n",
    "    if ptch_x <= med:\n",
    "        m_height = ptch.get_height() + 0.4\n",
    "    \n",
    "\n",
    "ax.set_xlabel(\"Результаты участников (минуты)\", size=14)\n",
    "ax.set_ylabel(\"Результатов в диапазоне\")\n",
    "\n",
    "ax.annotate(f'Медианное время\\n{int(med)}:{(med-int(med))*60:02.0f}', (med-2, m_height))\n",
    "plt.plot([med,med],[0,m_height], 'r')\n",
    "\n",
    "ldr_time = ptchs[0].get_x()\n",
    "ldr_y_mark = ptchs[0].get_height() + 0.4\n",
    "ax.annotate(f'Лидер {int(ldr_time)}:{(ldr_time-int(ldr_time))*60:02.0f}', (ldr_time, ldr_y_mark+0.5), rotation=45)\n",
    "plt.plot([ldr_time,ldr_time],[0,ldr_y_mark], 'r')\n",
    "\n",
    "lst_time = ptchs[-1].get_x() + ptchs[-1].get_width()\n",
    "lst_y_mark = ptchs[-1].get_height() + 0.4\n",
    "ax.annotate(f'Всего участников {number_runners}', (lst_time, lst_y_mark+0.5), rotation=90)\n",
    "plt.plot([lst_time,lst_time],[0,lst_y_mark], 'r')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('results.png')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Время лидера по женщинам"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "source": [
    "w_res = data[data['Пол'].str.contains('Женский')].reset_index(drop=True)\n",
    "w_res['result_m'][0]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "28.1"
      ]
     },
     "metadata": {},
     "execution_count": 240
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "source": [
    "\n",
    "personal_res = data[data['Участник'].str.contains('Усаче'.upper())].reset_index(drop=True)\n",
    "personal_res\n",
    "# if not personal_res.empty:\n",
    "#     personal_res = personal_res['result_m'][0]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Позиция, Участник, Пол, Возр. группа, Клуб, Время, Позиция м/ж, Личник, result_m]\n",
       "Index: []"
      ],
      "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>Позиция</th>\n",
       "      <th>Участник</th>\n",
       "      <th>Пол</th>\n",
       "      <th>Возр. группа</th>\n",
       "      <th>Клуб</th>\n",
       "      <th>Время</th>\n",
       "      <th>Позиция м/ж</th>\n",
       "      <th>Личник</th>\n",
       "      <th>result_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 241
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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  "kernelspec": {
   "name": "python386jvsc74a57bd05b3b84d9a651f6d852e680791b43bab4042960dedba8573afb8521cb6f212bd2",
   "language": "python",
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