personal_info.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import requests\n",
    "# import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, MaxNLocator\n",
    "from matplotlib.colors import Normalize, PowerNorm\n",
    "from matplotlib.patches import Patch\n",
    "# import seaborn as sns\n",
    "# sns.set(font_scale=1.2)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(athlete_id: int):\n",
    "    athlete_url = f'https://www.parkrun.ru/results/athleteeventresultshistory/?athleteNumber={athlete_id}&eventNumber=0'\n",
    "    page_all_results = requests.get(athlete_url, headers={'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:86.0) Gecko/20100101 Firefox/86.0'})\n",
    "    df = pd.read_html(page_all_results.text)[2]\n",
    "    df['Номер пробежки'] = df['Номер пробежки'].apply(int)\n",
    "    df['Место'] = df['Место'].apply(int)\n",
    "    df['time'] = df['Время'].transform(lambda time: sum(x * int(t) for x, t in zip([1/60, 1, 60], time.split(':')[::-1])))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_id = 875743\n",
    "my_df = get_df(my_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Паркран</th>\n",
       "      <th>Дата parkrun</th>\n",
       "      <th>Номер пробежки</th>\n",
       "      <th>Место</th>\n",
       "      <th>Время</th>\n",
       "      <th>Рейтинг</th>\n",
       "      <th>ЛР?</th>\n",
       "      <th>time</th>\n",
       "      <th>Год</th>\n",
       "      <th>Месяц</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>24/04/2021</td>\n",
       "      <td>207</td>\n",
       "      <td>21</td>\n",
       "      <td>23:09</td>\n",
       "      <td>58.10%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>2021</td>\n",
       "      <td>Апр</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tula Central</td>\n",
       "      <td>23/01/2021</td>\n",
       "      <td>178</td>\n",
       "      <td>2</td>\n",
       "      <td>20:00</td>\n",
       "      <td>67.25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2021</td>\n",
       "      <td>Янв</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tula Central</td>\n",
       "      <td>16/01/2021</td>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "      <td>20:11</td>\n",
       "      <td>66.64%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.183333</td>\n",
       "      <td>2021</td>\n",
       "      <td>Янв</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tula Central</td>\n",
       "      <td>09/01/2021</td>\n",
       "      <td>176</td>\n",
       "      <td>2</td>\n",
       "      <td>19:52</td>\n",
       "      <td>67.70%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.866667</td>\n",
       "      <td>2021</td>\n",
       "      <td>Янв</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tula Central</td>\n",
       "      <td>02/01/2021</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>21:39</td>\n",
       "      <td>62.12%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>2021</td>\n",
       "      <td>Янв</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Gorky Park</td>\n",
       "      <td>25/10/2014</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>18:07</td>\n",
       "      <td>71.76%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.116667</td>\n",
       "      <td>2014</td>\n",
       "      <td>Окт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Kolomenskoe</td>\n",
       "      <td>22/03/2014</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>19:14</td>\n",
       "      <td>67.42%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.233333</td>\n",
       "      <td>2014</td>\n",
       "      <td>Мар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Kolomenskoe</td>\n",
       "      <td>15/03/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>20:01</td>\n",
       "      <td>64.78%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.016667</td>\n",
       "      <td>2014</td>\n",
       "      <td>Мар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Kolomenskoe</td>\n",
       "      <td>08/03/2014</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18:59</td>\n",
       "      <td>68.31%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.983333</td>\n",
       "      <td>2014</td>\n",
       "      <td>Мар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Kolomenskoe</td>\n",
       "      <td>01/03/2014</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>18:57</td>\n",
       "      <td>68.43%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.950000</td>\n",
       "      <td>2014</td>\n",
       "      <td>Мар</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Паркран Дата parkrun  Номер пробежки  Место  Время Рейтинг  ЛР?  \\\n",
       "0        Kuzminki   24/04/2021             207     21  23:09  58.10%  NaN   \n",
       "1    Tula Central   23/01/2021             178      2  20:00  67.25%  NaN   \n",
       "2    Tula Central   16/01/2021             177      1  20:11  66.64%  NaN   \n",
       "3    Tula Central   09/01/2021             176      2  19:52  67.70%  NaN   \n",
       "4    Tula Central   02/01/2021             174      1  21:39  62.12%  NaN   \n",
       "..            ...          ...             ...    ...    ...     ...  ...   \n",
       "180    Gorky Park   25/10/2014              25      1  18:07  71.76%  NaN   \n",
       "181   Kolomenskoe   22/03/2014               4      2  19:14  67.42%  NaN   \n",
       "182   Kolomenskoe   15/03/2014               3     10  20:01  64.78%  NaN   \n",
       "183   Kolomenskoe   08/03/2014               2      2  18:59  68.31%  NaN   \n",
       "184   Kolomenskoe   01/03/2014               1      6  18:57  68.43%  NaN   \n",
       "\n",
       "          time   Год Месяц  \n",
       "0    23.150000  2021   Апр  \n",
       "1    20.000000  2021   Янв  \n",
       "2    20.183333  2021   Янв  \n",
       "3    19.866667  2021   Янв  \n",
       "4    21.650000  2021   Янв  \n",
       "..         ...   ...   ...  \n",
       "180  18.116667  2014   Окт  \n",
       "181  19.233333  2014   Мар  \n",
       "182  20.016667  2014   Мар  \n",
       "183  18.983333  2014   Мар  \n",
       "184  18.950000  2014   Мар  \n",
       "\n",
       "[185 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df['Год'] = pd.to_datetime(my_df['Дата parkrun'], dayfirst=True).dt.year\n",
    "my_df['Месяц'] = pd.to_datetime(my_df['Дата parkrun'], dayfirst=True).dt.month_name(locale='ru_RU.UTF-8').str.slice(stop=3)\n",
    "my_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "text": [
      "CPU times: user 3.38 s, sys: 3.48 s, total: 6.85 s\n",
      "Wall time: 11.7 s\n"
     ]
    }
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   "source": [
    "%%time\n",
    "rundata_long = my_df.pivot_table(index='Месяц', columns='Год', values='Время', aggfunc=len, fill_value=0).astype(int)\n",
    "months = ['Янв','Фев','Мар','Апр','Мая','Июн','Июл','Авг','Сен','Окт','Ноя','Дек']\n",
    "for month in months:\n",
    "    if month not in rundata_long.index.values:\n",
    "        rundata_long.loc[month] = 0\n",
    "# sort chronological\n",
    "rundata_long = rundata_long.reindex(months)\n",
    "n_years = rundata_long.shape[1]\n",
    "fig = plt.figure(figsize=(6, 6), dpi=100)\n",
    "ax = plt.gca(xlabel='Год', ylabel='Месяц')\n",
    "im = ax.imshow(rundata_long, cmap=\"Greens\")\n",
    "cbar = ax.figure.colorbar(im, ax=ax)\n",
    "cbar.ax.set_ylabel('паркранов в месяц', rotation=90, va='top')\n",
    "# Setting minor grid\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "ax.set_xticks([i-.5 for i in range(n_years)], minor=True)\n",
    "ax.set_yticks([i-.5 for i in range(12)], minor=True)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "# Setting the labels\n",
    "ax.set_xticks(list(range(n_years)))\n",
    "ax.set_yticks(list(range(12)))\n",
    "# Labeling respective list entries\n",
    "ax.set_xticklabels(rundata_long.columns.values)\n",
    "ax.set_yticklabels(months)\n",
    "# Creating text annotations\n",
    "for i in range(12):\n",
    "    for j in range(n_years):\n",
    "        val = rundata_long.iloc[i, j]\n",
    "        ax.text(j, i, val, ha=\"center\", va=\"center\", color='w' if val > 2 else 'k')\n",
    "ax.set_title(\"История участия в паркранах\", fontweight='bold')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 185 entries, 0 to 184\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Паркран         185 non-null    object \n",
      " 1   Дата parkrun    185 non-null    object \n",
      " 2   Номер пробежки  185 non-null    int64  \n",
      " 3   Место           185 non-null    int64  \n",
      " 4   Время           185 non-null    object \n",
      " 5   Рейтинг         185 non-null    object \n",
      " 6   ЛР?             22 non-null     object \n",
      " 7   time            185 non-null    float64\n",
      "dtypes: float64(1), int64(2), object(5)\n",
      "memory usage: 11.7+ KB\n"
     ]
    }
   ],
   "source": [
    "my_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vizavi_df = get_df(875431)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Паркран</th>\n",
       "      <th>Дата parkrun</th>\n",
       "      <th>Номер пробежки</th>\n",
       "      <th>Место</th>\n",
       "      <th>Время</th>\n",
       "      <th>Рейтинг</th>\n",
       "      <th>ЛР?</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>24/04/2021</td>\n",
       "      <td>207</td>\n",
       "      <td>15</td>\n",
       "      <td>21:39</td>\n",
       "      <td>59.58%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>17/04/2021</td>\n",
       "      <td>206</td>\n",
       "      <td>53</td>\n",
       "      <td>23:54</td>\n",
       "      <td>53.97%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>10/04/2021</td>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>17:03</td>\n",
       "      <td>75.66%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>03/04/2021</td>\n",
       "      <td>204</td>\n",
       "      <td>17</td>\n",
       "      <td>22:31</td>\n",
       "      <td>57.29%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>14/03/2020</td>\n",
       "      <td>203</td>\n",
       "      <td>49</td>\n",
       "      <td>23:24</td>\n",
       "      <td>55.13%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.400000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>23/07/2016</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>18:32</td>\n",
       "      <td>70.32%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Kuzminki</td>\n",
       "      <td>18/06/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17:03</td>\n",
       "      <td>76.44%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Gorky Park</td>\n",
       "      <td>15/08/2015</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>17:24</td>\n",
       "      <td>75.77%</td>\n",
       "      <td>ЛР</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Gorky Park</td>\n",
       "      <td>01/08/2015</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>17:44</td>\n",
       "      <td>74.34%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Gorky Park</td>\n",
       "      <td>25/07/2015</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>17:36</td>\n",
       "      <td>74.91%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Паркран Дата parkrun  Номер пробежки  Место  Время Рейтинг  ЛР?  \\\n",
       "0      Kuzminki   24/04/2021             207     15  21:39  59.58%  NaN   \n",
       "1      Kuzminki   17/04/2021             206     53  23:54  53.97%  NaN   \n",
       "2      Kuzminki   10/04/2021             205      1  17:03  75.66%  NaN   \n",
       "3      Kuzminki   03/04/2021             204     17  22:31  57.29%  NaN   \n",
       "4      Kuzminki   14/03/2020             203     49  23:24  55.13%  NaN   \n",
       "..          ...          ...             ...    ...    ...     ...  ...   \n",
       "106    Kuzminki   23/07/2016               6      4  18:32  70.32%  NaN   \n",
       "107    Kuzminki   18/06/2016               1      3  17:03  76.44%  NaN   \n",
       "108  Gorky Park   15/08/2015              67      2  17:24  75.77%   ЛР   \n",
       "109  Gorky Park   01/08/2015              65      2  17:44  74.34%  NaN   \n",
       "110  Gorky Park   25/07/2015              64      1  17:36  74.91%  NaN   \n",
       "\n",
       "          time  \n",
       "0    21.650000  \n",
       "1    23.900000  \n",
       "2    17.050000  \n",
       "3    22.516667  \n",
       "4    23.400000  \n",
       "..         ...  \n",
       "106  18.533333  \n",
       "107  17.050000  \n",
       "108  17.400000  \n",
       "109  17.733333  \n",
       "110  17.600000  \n",
       "\n",
       "[111 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vizavi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "battle_df = pd.merge(my_df, vizavi_df, on=['Дата parkrun', 'Паркран'])\n",
    "battle_df['time_diff'] = battle_df['time_x'] - battle_df['time_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wins = battle_df['time_x'] < battle_df['time_y']\n",
    "cor = pd.value_counts(wins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(battle_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51 entries, 0 to 50\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Паркран           51 non-null     object \n",
      " 1   Дата parkrun      51 non-null     object \n",
      " 2   Номер пробежки_x  51 non-null     int64  \n",
      " 3   Место_x           51 non-null     int64  \n",
      " 4   Время_x           51 non-null     object \n",
      " 5   Рейтинг_x         51 non-null     object \n",
      " 6   ЛР?_x             4 non-null      object \n",
      " 7   time_x            51 non-null     float64\n",
      " 8   Номер пробежки_y  51 non-null     int64  \n",
      " 9   Место_y           51 non-null     int64  \n",
      " 10  Время_y           51 non-null     object \n",
      " 11  Рейтинг_y         51 non-null     object \n",
      " 12  ЛР?_y             1 non-null      object \n",
      " 13  time_y            51 non-null     float64\n",
      " 14  time_diff         51 non-null     float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 6.4+ KB\n"
     ]
    }
   ],
   "source": [
    "battle_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlsx_df = battle_df.iloc[:, [0,1,3,4,6,9,10,12]]\n",
    "xlsx_df.rename(columns={\n",
    "    'Место_x': 'Ваше место', \n",
    "    'Время_x': 'Ваше время', \n",
    "    'ЛР?_x': 'Личник?', \n",
    "    'Место_y': 'Место ', \n",
    "    'Время_y': 'Время ', \n",
    "    'ЛР_?': 'Личник?'}, inplace=True)\n",
    "# xlsx_df.to_excel(\"output.xlsx\", index=False, sheet_name='Сравнение результатов')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t24/04/2021\t23:09\t21:39\tKuzminki\n",
      "1\t21/12/2019\t18:43\t20:59\tKuzminki\n",
      "2\t14/12/2019\t18:08\t23:22\tKuzminki\n",
      "3\t30/11/2019\t17:51\t20:21\tKuzminki\n",
      "4\t23/11/2019\t17:52\t19:37\tKuzminki\n",
      "5\t09/11/2019\t17:08\t16:47\tKuzminki\n",
      "6\t02/11/2019\t17:37\t19:04\tKuzminki\n",
      "7\t26/10/2019\t17:27\t16:38\tKuzminki\n",
      "8\t12/10/2019\t17:38\t16:09\tKuzminki\n",
      "9\t28/09/2019\t17:26\t16:45\tKuzminki\n",
      "10\t07/09/2019\t21:02\t16:43\tKuzminki\n",
      "11\t18/05/2019\t25:13\t22:29\tKuzminki\n",
      "12\t04/05/2019\t17:44\t23:27\tKuzminki\n",
      "13\t20/04/2019\t19:33\t25:00\tKuzminki\n",
      "14\t27/10/2018\t17:22\t28:15\tKuzminki\n",
      "15\t20/10/2018\t23:08\t23:16\tKuzminki\n",
      "16\t13/10/2018\t17:40\t25:42\tKuzminki\n",
      "17\t29/09/2018\t17:32\t25:40\tKuzminki\n",
      "18\t22/09/2018\t33:01\t28:17\tKuzminki\n",
      "19\t15/09/2018\t17:36\t28:36\tKuzminki\n",
      "20\t08/09/2018\t17:59\t26:37\tKuzminki\n",
      "21\t25/08/2018\t17:06\t26:12\tKuzminki\n",
      "22\t11/08/2018\t17:16\t16:41\tKuzminki\n",
      "23\t30/06/2018\t17:21\t27:49\tKuzminki\n",
      "24\t16/06/2018\t30:39\t16:35\tKuzminki\n",
      "25\t09/06/2018\t17:16\t22:14\tKuzminki\n",
      "26\t07/01/2018\t17:50\t24:37\tKuzminki\n",
      "27\t30/12/2017\t17:49\t23:17\tKuzminki\n",
      "28\t02/12/2017\t18:54\t24:03\tKuzminki\n",
      "29\t25/11/2017\t17:54\t24:44\tKuzminki\n",
      "30\t11/11/2017\t17:23\t17:17\tKuzminki\n",
      "31\t28/10/2017\t17:49\t17:12\tKuzminki\n",
      "32\t14/10/2017\t17:46\t22:00\tKuzminki\n",
      "33\t07/10/2017\t17:38\t24:22\tKuzminki\n",
      "34\t30/09/2017\t23:58\t23:36\tKuzminki\n",
      "35\t23/09/2017\t25:58\t25:46\tKuzminki\n",
      "36\t17/06/2017\t17:37\t16:30\tKuzminki\n",
      "37\t03/06/2017\t17:24\t16:40\tKuzminki\n",
      "38\t06/05/2017\t17:21\t16:40\tKuzminki\n",
      "39\t01/04/2017\t18:28\t18:42\tKuzminki\n",
      "40\t11/03/2017\t19:35\t17:51\tKuzminki\n",
      "41\t03/12/2016\t30:17\t18:22\tKuzminki\n",
      "42\t29/10/2016\t19:47\t17:53\tKuzminki\n",
      "43\t22/10/2016\t18:09\t16:24\tKuzminki\n",
      "44\t15/10/2016\t17:54\t17:30\tKuzminki\n",
      "45\t08/10/2016\t18:10\t16:59\tKuzminki\n",
      "46\t01/10/2016\t18:50\t17:09\tKuzminki\n",
      "47\t10/09/2016\t21:15\t16:44\tKuzminki\n",
      "48\t03/09/2016\t20:08\t17:19\tKuzminki\n",
      "49\t20/08/2016\t21:52\t17:35\tKuzminki\n",
      "50\t18/06/2016\t20:53\t17:03\tKuzminki\n"
     ]
    }
   ],
   "source": [
    "for i, row in battle_df.iterrows():\n",
    "    print(i, row['Дата parkrun'], row['Время_x'], row['Время_y'], row['Паркран'], sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-60.05000000000004"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "battle_df['time_diff'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {},
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   ],
   "source": [
    "battle_df = battle_df.head(10)\n",
    "def label_bars(marks, heights, rects):\n",
    "    for mark, height, rect in zip(marks, heights, rects):\n",
    "        ax.annotate(f'{mark}',\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset.\n",
    "                    textcoords='offset points',\n",
    "                    ha='center', va='bottom', size=8, clip_on=True)\n",
    "\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "fig.patch.set_facecolor('#eeeeee')\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "xlabels = battle_df['Дата parkrun']\n",
    "x = xlabels.index\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(xlabels, rotation=70)\n",
    "spacing = 0.3  # spacing between hat groups\n",
    "width = (1 - spacing) / 2\n",
    "heights0 = battle_df['time_x'].combine(battle_df['time_y'], min)\n",
    "rects = ax.bar(x - spacing/2, battle_df['time_x'] - heights0, width, bottom=heights0, label='Проигрыш', fill=False, edgecolor='#1f77b4')\n",
    "label_bars(battle_df['Время_x'], battle_df['time_x'], rects)\n",
    "rects = ax.bar(x + 0.5 - spacing, battle_df['time_y'] - heights0, width, bottom=heights0, label='Победа', edgecolor='#1f77b4')\n",
    "label_bars(battle_df['Время_y'], battle_df['time_y'], rects)\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Последние 10 паркранов', fontweight='bold')\n",
    "ax.set_ylabel('Результат')\n",
    "ax.set_ylim(int(min(battle_df['time_x'].min(), battle_df['time_y'].min()))-1, \n",
    "int(max(battle_df['time_x'].max(), battle_df['time_y'].max()))+1)\n",
    "ax.set_title(f'Соотношение результатов\\nна совместных забегах {cor[True]}:{cor[False]}', size=15, fontweight='bold')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('battle.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "battle_df = battle_df.head(10)\n",
    "def label_bars(marks, heights, rects, wins, color):\n",
    "    for mark, height, rect, win in zip(marks, heights, rects, wins):\n",
    "        ax.annotate(f'{mark}',\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, -10 if win else 2),  # 3 points vertical offset.\n",
    "                    textcoords='offset points',\n",
    "                    ha='center', va='bottom', size=8, color=color, fontweight='bold')\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4.5))\n",
    "fig.patch.set_facecolor('#eeeeee')\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "xlabels = battle_df['Дата parkrun']\n",
    "x = xlabels.index\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(xlabels, rotation=70)\n",
    "spacing = 0.3  # spacing between hat groups\n",
    "width = (1 - spacing) / 2\n",
    "heights0 = battle_df['time_x'].combine(battle_df['time_y'], min)\n",
    "heights1 = battle_df['time_x'].combine(battle_df['time_y'], max)\n",
    "\n",
    "rects = ax.bar(x, heights1 - heights0, 0.6, bottom=heights0, label='Выигрыш', edgecolor='black', color=wins.where(wins, '#ff7f0e').where(~wins, '#2ca02c'))\n",
    "label_bars(battle_df['Время_y'], battle_df['time_y'], rects, ~wins, '#7f7f7f')\n",
    "label_bars(battle_df['Время_x'], battle_df['time_x'], rects, wins, 'black')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Последние 10 паркранов', fontweight='bold')\n",
    "ax.set_ylabel('Результат')\n",
    "ax.set_ylim(int(min(battle_df['time_x'].min(), battle_df['time_y'].min()))-1, \n",
    "int(max(battle_df['time_x'].max(), battle_df['time_y'].max()))+1)\n",
    "ax.set_title(f'Соотношение результатов\\nна совместных забегах {cor[True]}:{cor[False]}', size=15, fontweight='bold')\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('battle.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "ax = fig.add_subplot()\n",
    "ax.scatter(battle_df['time_x'], battle_df['time_y'], c=wins.where(wins, '#ff7f0e').where(~wins, '#2ca02c'))\n",
    "plt.plot([17, 33], [17, 33], color='r', alpha=0.5)\n",
    "ax.set_xlabel('Юрий Володин', fontweight='bold')\n",
    "ax.set_ylabel('Герман Жаров', fontweight='bold')\n",
    "ax.xaxis.set_major_locator(MultipleLocator(2))\n",
    "ax.yaxis.set_major_locator(MultipleLocator(2))\n",
    "ax.set_title(f'Соотношение результатов\\nна совместных забегах {cor[True]}:{cor[False]}', size=15, fontweight='bold')\n",
    "legend_elements = [Patch(facecolor='#ff7f0e', label='Проигрыш'),\n",
    "                       Patch(facecolor='#2ca02c', label='Выигрыш')]\n",
    "ax.legend(handles=legend_elements)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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