ski_name_scrapper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all the libraries\n",
    "from bs4 import BeautifulSoup\n",
    "import requests as req\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "venues = {\n",
    "    'xtremeverbierswitzerland' : 'Verbier',\n",
    "    'ordinoarcalisandorra' : 'Ordino Arcalis',\n",
    "    'fieberbrunnaustria' : 'Fieberbrunn',\n",
    "    'kickinghorsegoldenbc' : 'Kicking Horse',\n",
    "    'hakubajapan' : 'Hakuba',\n",
    "    'vallnordarcalisandorra' : 'Ordino Arcalis',\n",
    "    'xtremeverbier' : 'Verbier',\n",
    "    'hainesalaska' : 'Haines',\n",
    "    'fieberbrunn' : 'Fieberbrunn',\n",
    "    'chamonixmontblancstagedvallnordarcalisandorra' : 'Chamonix Montblanc',\n",
    "    'fieberbrunnkitzbuheleralpen' : 'Fieberbrunn',\n",
    "    'chamonixmontblanc' : 'Chamonix Montblanc',\n",
    "    'alaska' : 'Haines',\n",
    "    'fieberbrunnkitzbueheleralpen' : 'Fieberbrunn',\n",
    "    'snowbird' : 'Snowbird',\n",
    "    'chamonix' : 'Chamonix Montblanc',\n",
    "    'courmayeur' : 'Courmayeur',\n",
    "    'verbierxtreme' : 'Verbier',\n",
    "    'fieberbrunnpillerseetal' : 'Fieberbrunn',\n",
    "    'kirkwood' : 'Kirkwood',\n",
    "    'courmayeurmontblanc' : 'Courmayeur',\n",
    "    'revelstoke' : 'Revelstoke',\n",
    "    'roldal' : 'Roldal',\n",
    "    'crystalmountain' : 'Crystal',\n",
    "    'sochi' : 'Sochi',\n",
    "    'engadinsnow' : 'Engadin',\n",
    "    'squawvalley' : 'Squaw Valley',\n",
    "    'tignes' : 'Tignes',\n",
    "    'mammoth' : 'Mammoth'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip(string):\n",
    "    test = string.replace('-','')\n",
    "\n",
    "    test = test.replace('freerideworldtour','')\n",
    "    test = test.replace('swatch','')\n",
    "    test = test.replace('northface','')\n",
    "    test = test.replace('tnf','')\n",
    "    test = re.sub('\\d','',test)\n",
    "    return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all the links read from a file\n",
    "file = open('links.txt', 'r')\n",
    "raw_links = file.readlines()\n",
    "links = []\n",
    "# strip the /n chars\n",
    "for link in raw_links:\n",
    "    links.append(link.strip())\n",
    "file.close()\n",
    "\n",
    "# number of male riders per event \n",
    "numbers = [13,13,20,20,21,22,24,24,25,25,13,12,23,25,24,14,25,25,29,15,16,29,32,29,15,29,32,35,32,13,29,\n",
    "           31,33,33,28,14,21,21,22,23,23,15,20,19,19,21,21,8,19,22,22,18,22,20,21,16,19,17,19,14,12,11,10]\n",
    "\n",
    "# some numbers were duplicates on the 2019 websites. This is just a catch exception annoying but what can u do\n",
    "dupe = [(True,0),(False,0),(True,4),(True,9),(True,9),(True,20),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),\n",
    "        (False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),\n",
    "        (False,0),(False,0), (False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),\n",
    "        (False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),\n",
    "       (False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),\n",
    "       (False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0),(False,0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_data(event,dupe_status,duplicate,number_of):\n",
    "    \n",
    "    # get content and analyze data\n",
    "    url = \"https://www.freerideworldtour.com/event/{}/results\".format(event)\n",
    "    r = req.get(url)\n",
    "    html = r.content\n",
    "    soup = BeautifulSoup(html,'html.parser')\n",
    "    name_find = soup.find_all('span',{\"class\":'name'})\n",
    "    pos_find = soup.find_all('span',{\"class\": \"position\"})\n",
    "    score_find = soup.find_all('span',{\"class\":'total-points'})\n",
    "    \n",
    "    # parse scraped data into arrays after getting text and stripping \n",
    "    names = []\n",
    "    pos = []\n",
    "    score = []\n",
    "    for name in name_find:\n",
    "        names.append(name.getText().strip())\n",
    "    for position in pos_find:\n",
    "        pos.append(position.getText().strip())\n",
    "    for scores in score_find:\n",
    "        score.append(scores.getText().strip())\n",
    "        \n",
    "    # delete any duplicate characters (manual operation)\n",
    "    if dupe_status is True:\n",
    "        del pos[duplicate]\n",
    "    \n",
    "    # create data frame\n",
    "    event_results = pd.DataFrame()\n",
    "    skiers = names[0:number_of]\n",
    "    event_results['Name'] = pd.Series(skiers)\n",
    "    event_results['Position'] = pd.Series(pos)\n",
    "    event_results['Score-a'] = pd.Series(score)\n",
    "    # translate event link using dict\n",
    "    event_results['Event'] = venues[strip(event)]\n",
    "    # year is usually at the end except for three cases see clean_up()\n",
    "    #\n",
    "    #\n",
    "    event_results['Year'] = re.sub('\\D', '', event)\n",
    "    \n",
    "    return event_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_up():\n",
    "    \n",
    "    \n",
    "    test_lists = []\n",
    "    # concatenate the data after creating the 'big set'\n",
    "    for i in range(0,len(numbers)):\n",
    "        test_lists.append(fill_data(links[i],dupe[i][0],dupe[i][1],numbers[i]))\n",
    "    \n",
    "    frame = pd.concat(test_lists).reset_index(drop=True)\n",
    "    \n",
    "    # replace text with years for exception links\n",
    "    \n",
    "    # translate scores to numeric using a string cleanup\n",
    "    num_scores = []\n",
    "    for score in frame['Score-a']:\n",
    "        num_scores.append(score[:-5])\n",
    "    \n",
    "    # fit in the DataFrame\n",
    "    frame['Score'] = pd.Series(num_scores)\n",
    "    frame['Score'] = pd.to_numeric(frame['Score'])\n",
    "    frame['Position'] = pd.to_numeric(frame['Position'])\n",
    "    \n",
    "    return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_tables():\n",
    "    frame = f\n",
    "    unq_name = frame['Name'].unique()\n",
    "    mean_pos = []\n",
    "    std_dev = []\n",
    "    appearances = []\n",
    "    mean_points = []\n",
    "    \n",
    "    for name in unq_name:\n",
    "        placeholder = frame[frame['Name'].eq(name)]\n",
    "        appearances.append(len(frame[frame['Name'].eq(name)]))\n",
    "        mean_pos.append(round(placeholder['Position'].mean(),0))\n",
    "        std_dev.append(np.std(placeholder['Position']))\n",
    "        mean_points.append(placeholder['Score'].mean())\n",
    "        \n",
    "    mean_frame = pd.DataFrame()\n",
    "    mean_frame['Name'] = pd.Series(unq_name)\n",
    "    mean_frame['Appearances'] = pd.Series(appearances)\n",
    "    mean_frame['Mean'] = pd.Series(mean_pos)\n",
    "    mean_frame['Standard Deviation'] = pd.Series(std_dev)\n",
    "    mean_frame['Mean Score'] = pd.Series(mean_points)\n",
    "    \n",
    "    return mean_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = clean_up()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "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>Name</th>\n",
       "      <th>Appearances</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Mean Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wadeck Gorak</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.059745</td>\n",
       "      <td>1365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leo Slemett</td>\n",
       "      <td>28</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.616178</td>\n",
       "      <td>1293.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kristofer Turdell</td>\n",
       "      <td>19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.923333</td>\n",
       "      <td>1679.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reine Barkered</td>\n",
       "      <td>55</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.860354</td>\n",
       "      <td>1514.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mickael Bimboes</td>\n",
       "      <td>14</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.109603</td>\n",
       "      <td>1209.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Schroder Baker</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Chris Davenport</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Jean-Yves Michellod</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Edouard Baud</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Arto Majava</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1105.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  Appearances  Mean  Standard Deviation   Mean Score\n",
       "0           Wadeck Gorak            5  10.0            7.059745  1365.000000\n",
       "1            Leo Slemett           28  11.0            8.616178  1293.571429\n",
       "2      Kristofer Turdell           19   6.0            5.923333  1679.210526\n",
       "3         Reine Barkered           55   8.0            6.860354  1514.090909\n",
       "4        Mickael Bimboes           14  13.0           10.109603  1209.357143\n",
       "..                   ...          ...   ...                 ...          ...\n",
       "136       Schroder Baker            1  11.0            0.000000  1025.000000\n",
       "137      Chris Davenport            1   3.0            0.000000  1980.000000\n",
       "138  Jean-Yves Michellod            2   4.0            3.000000  1940.000000\n",
       "139         Edouard Baud            1   5.0            0.000000  1640.000000\n",
       "140          Arto Majava            1  10.0            0.000000  1105.000000\n",
       "\n",
       "[141 rows x 5 columns]"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_1 = freq_tables()\n",
    "f_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"Appearances\", \"Standard Deviation\", data=f_1, \n",
    "                  kind=\"reg\", truncate=False,\n",
    "                  xlim=(0, 56), ylim=(0, 14), color=\"m\", height=7)\n",
    "plt.title(\"Relation between Standard Deviation and Number of Appearances (2004-2019)\")\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Number of FWT event appearances')\n",
    "plt.ylabel('Standard Deviation of Achived Positions')\n",
    "plt.annotate('Drew Tabke', xy=(49, 6.9), xytext=(30, 1),arrowprops=dict(facecolor='black', shrink=0.06))\n",
    "plt.annotate('Reine Barkered', xy=(55.3, 7), xytext=(45, 1),arrowprops=dict(facecolor='black', shrink=0.06))\n",
    "plt.savefig('std.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "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": [
    "plot = f_1.sort_values(by='Mean',ascending=True)[0:10]\n",
    "plt.barh(plot['Name'], plot['Mean'],align='center')\n",
    "plt.title(\"Top-10 Best Mean Positions Achived FWT (2004-2019)\")\n",
    "plt.xlabel('Mean Position Achived (lower is better)')\n",
    "plt.savefig('best_mean.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}