notebook_cleaned_commented.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so essentially this jupyter notebook has the code I used to \n",
    "#    a)download pages from IAAF.org,\n",
    "#    b)scrape data from them\n",
    "#    c) analyse that data\n",
    "#    d) produce csvs to visualise in datawrapper/tableau (no d3 coding this time)\n",
    "\n",
    "# most of the cell blocks will have commented code at the top of the cell explaining what they do\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.mkdir('event_pages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.mkdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>gender</th>\n",
       "      <th>national_record</th>\n",
       "      <th>national_name</th>\n",
       "      <th>national_venue</th>\n",
       "      <th>national_date</th>\n",
       "      <th>oly_bronze_perf</th>\n",
       "      <th>oly_bronze_name</th>\n",
       "      <th>oly_bronze_country</th>\n",
       "      <th>asiad_bronze_perf</th>\n",
       "      <th>...</th>\n",
       "      <th>wr_name</th>\n",
       "      <th>wr_country</th>\n",
       "      <th>wr_venue</th>\n",
       "      <th>wr_date</th>\n",
       "      <th>asian_record</th>\n",
       "      <th>ar_name</th>\n",
       "      <th>ar_country</th>\n",
       "      <th>ar_venue</th>\n",
       "      <th>ar_date</th>\n",
       "      <th>asiad_gold_perf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100 metres</td>\n",
       "      <td>men</td>\n",
       "      <td>10.21</td>\n",
       "      <td>Anil KUMAR</td>\n",
       "      <td>Bangalore (IND)</td>\n",
       "      <td>05/07/2000</td>\n",
       "      <td>9.91</td>\n",
       "      <td>Andre De Grasse</td>\n",
       "      <td>CAN</td>\n",
       "      <td>10.15</td>\n",
       "      <td>...</td>\n",
       "      <td>Usain BOLT</td>\n",
       "      <td>JAM</td>\n",
       "      <td>Berlin (Olympiastadion)</td>\n",
       "      <td>16/08/2009</td>\n",
       "      <td>9.87</td>\n",
       "      <td>Yoshihide KIRYU</td>\n",
       "      <td>JPN</td>\n",
       "      <td>Austin, TX (USA)</td>\n",
       "      <td>28/03/2015</td>\n",
       "      <td>9.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>men</td>\n",
       "      <td>20.66</td>\n",
       "      <td>Dharmbir SINGH</td>\n",
       "      <td>Wuhan (CHN)</td>\n",
       "      <td>07/06/2015</td>\n",
       "      <td>20.12</td>\n",
       "      <td>Christophe LEMAITRE</td>\n",
       "      <td>FRA</td>\n",
       "      <td>20.82</td>\n",
       "      <td>...</td>\n",
       "      <td>Usain BOLT</td>\n",
       "      <td>JAM</td>\n",
       "      <td>Berlin (Olympiastadion)</td>\n",
       "      <td>20/08/2009</td>\n",
       "      <td>19.97</td>\n",
       "      <td>Femi OGUNODE</td>\n",
       "      <td>QAT</td>\n",
       "      <td>Bruxelles (BEL)</td>\n",
       "      <td>11/09/2015</td>\n",
       "      <td>20.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>400 metres</td>\n",
       "      <td>men</td>\n",
       "      <td>45.24</td>\n",
       "      <td>Mohammed Anas YAHIYA</td>\n",
       "      <td>Noveho Mesta nad Metuji (CZE)</td>\n",
       "      <td>21/07/2018</td>\n",
       "      <td>43.85</td>\n",
       "      <td>LaShawn Merritt</td>\n",
       "      <td>USA</td>\n",
       "      <td>45.92</td>\n",
       "      <td>...</td>\n",
       "      <td>Wayde VAN NIEKERK</td>\n",
       "      <td>RSA</td>\n",
       "      <td>Rio de Janeiro (Est dio Ol¡mpico)</td>\n",
       "      <td>14/08/2016</td>\n",
       "      <td>43.93</td>\n",
       "      <td>Youssef Ahmed MASRAHI</td>\n",
       "      <td>KSA</td>\n",
       "      <td>Beijing (CHN)</td>\n",
       "      <td>23/08/2015</td>\n",
       "      <td>44.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>800 metres</td>\n",
       "      <td>men</td>\n",
       "      <td>1:45.65</td>\n",
       "      <td>Jinson JOHNSON</td>\n",
       "      <td>Guwahati (IND)</td>\n",
       "      <td>27/06/2018</td>\n",
       "      <td>01:42.93</td>\n",
       "      <td>Clayton Murphy</td>\n",
       "      <td>USA</td>\n",
       "      <td>01:48.25</td>\n",
       "      <td>...</td>\n",
       "      <td>David RUDISHA</td>\n",
       "      <td>KEN</td>\n",
       "      <td>London (Olympic Stadium)</td>\n",
       "      <td>09/08/2012</td>\n",
       "      <td>01:42.79</td>\n",
       "      <td>Yusuf Saad KAMEL</td>\n",
       "      <td>BRN</td>\n",
       "      <td>Monaco (MON)</td>\n",
       "      <td>29/07/2008</td>\n",
       "      <td>01:47.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500 metres</td>\n",
       "      <td>men</td>\n",
       "      <td>03:37.86</td>\n",
       "      <td>Jinson JOHNSON</td>\n",
       "      <td>Gold Coast (AUS)</td>\n",
       "      <td>14/04/2018</td>\n",
       "      <td>03:50.24</td>\n",
       "      <td>Nicholas Willis</td>\n",
       "      <td>NZL</td>\n",
       "      <td>03:42.50</td>\n",
       "      <td>...</td>\n",
       "      <td>Hicham EL GUERROUJ</td>\n",
       "      <td>MAR</td>\n",
       "      <td>Roma (Stadio Olimpico)</td>\n",
       "      <td>14/07/1998</td>\n",
       "      <td>03:29.14</td>\n",
       "      <td>Rashid RAMZI</td>\n",
       "      <td>BRN</td>\n",
       "      <td>Roma (ITA)</td>\n",
       "      <td>14/07/2006</td>\n",
       "      <td>03:40.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         event gender national_record         national_name  \\\n",
       "0   100 metres    men           10.21            Anil KUMAR   \n",
       "1   200 metres    men           20.66        Dharmbir SINGH   \n",
       "2   400 metres    men           45.24  Mohammed Anas YAHIYA   \n",
       "3   800 metres    men         1:45.65        Jinson JOHNSON   \n",
       "4  1500 metres    men        03:37.86        Jinson JOHNSON   \n",
       "\n",
       "                  national_venue national_date oly_bronze_perf  \\\n",
       "0                Bangalore (IND)    05/07/2000            9.91   \n",
       "1                    Wuhan (CHN)    07/06/2015           20.12   \n",
       "2  Noveho Mesta nad Metuji (CZE)    21/07/2018           43.85   \n",
       "3                 Guwahati (IND)    27/06/2018        01:42.93   \n",
       "4               Gold Coast (AUS)    14/04/2018        03:50.24   \n",
       "\n",
       "       oly_bronze_name oly_bronze_country asiad_bronze_perf       ...        \\\n",
       "0      Andre De Grasse                CAN             10.15       ...         \n",
       "1  Christophe LEMAITRE                FRA             20.82       ...         \n",
       "2      LaShawn Merritt                USA             45.92       ...         \n",
       "3       Clayton Murphy                USA          01:48.25       ...         \n",
       "4      Nicholas Willis                NZL          03:42.50       ...         \n",
       "\n",
       "              wr_name wr_country                           wr_venue  \\\n",
       "0          Usain BOLT        JAM            Berlin (Olympiastadion)   \n",
       "1          Usain BOLT        JAM            Berlin (Olympiastadion)   \n",
       "2   Wayde VAN NIEKERK        RSA  Rio de Janeiro (Est dio Ol¡mpico)   \n",
       "3       David RUDISHA        KEN           London (Olympic Stadium)   \n",
       "4  Hicham EL GUERROUJ        MAR             Roma (Stadio Olimpico)   \n",
       "\n",
       "      wr_date asian_record                ar_name ar_country  \\\n",
       "0  16/08/2009         9.87        Yoshihide KIRYU        JPN   \n",
       "1  20/08/2009        19.97           Femi OGUNODE        QAT   \n",
       "2  14/08/2016        43.93  Youssef Ahmed MASRAHI        KSA   \n",
       "3  09/08/2012     01:42.79       Yusuf Saad KAMEL        BRN   \n",
       "4  14/07/1998     03:29.14           Rashid RAMZI        BRN   \n",
       "\n",
       "           ar_venue     ar_date asiad_gold_perf  \n",
       "0  Austin, TX (USA)  28/03/2015            9.93  \n",
       "1   Bruxelles (BEL)  11/09/2015           20.14  \n",
       "2     Beijing (CHN)  23/08/2015           44.46  \n",
       "3      Monaco (MON)  29/07/2008        01:47.48  \n",
       "4        Roma (ITA)  14/07/2006        03:40.23  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compiled the csv record_list by hand from various websites -- indianathletics.in for national records,\n",
    "#iaaf.org for world records\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "records_df = pd.read_csv('data/record_list.csv', encoding='latin-1')\n",
    "\n",
    "records_df.head()\n",
    "\n",
    "#this is what it looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the national_date and wr_date columns into datetime,\n",
    "    #meaning i'm asking python to recognise that column as a date and not a simple text string\n",
    "\n",
    "records_df['national_date'] = pd.to_datetime(records_df['national_date'],format='%d/%m/%Y')\n",
    "\n",
    "records_df['wr_date'] = pd.to_datetime(records_df['wr_date'],format='%d/%m/%Y')\n",
    "\n",
    "records_df['ar_date'] = pd.to_datetime(records_df['wr_date'],format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is just a dictionary i am creating that will help me later on. So different events are measured in \n",
    "    #either time (100 m, 200m etc.) or distance (long jump, shot put) or points (heptathlon, decathlon)\n",
    "    #depending on whether they are units of time, distance or just points, the calculations will\n",
    "    #take place differently\n",
    "\n",
    "measure_dict={\n",
    "'100 metres':'time',\n",
    "'200 metres':'time',\n",
    "'400 metres':'time',\n",
    "'100 metres hurdles':'time',\n",
    "'110 metres hurdles':'time',\n",
    "'400 metres hurdles':'time',\n",
    "'800 metres':'time',\n",
    "'1500 metres':'time',\n",
    "'5000 metres':'time',\n",
    "'10000 metres':'time',\n",
    "'3000 metres steeplechase':'time',\n",
    "'20 kilometres race walk':'time',\n",
    "'50 kilometres race walk':'time',\n",
    "'Marathon':'time',\n",
    "'High jump':'distance',\n",
    "'Long jump':'distance',\n",
    "'Triple jump':'distance',\n",
    "'Shot put':'distance',\n",
    "'Discus throw':'distance',\n",
    "'Hammer throw':'distance',\n",
    "'Javelin throw':'distance',\n",
    "'Pole vault':'distance',\n",
    "'Heptathlon':'points',\n",
    "'Decathlon':'points'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "##### DONE AND DONE ####\n"
     ]
    }
   ],
   "source": [
    "#in this cell block, essentially I'm using python to create some new columns, where 9.58 is not a text string\n",
    "    #but properly recognised as 9.58 seconds\n",
    "\n",
    "from datetime import datetime,time\n",
    "from datetime import timedelta\n",
    "import re\n",
    "\n",
    "for index,row in records_df.iterrows():\n",
    "    \n",
    "    print(index)\n",
    "    \n",
    "    event = row['event']\n",
    "    \n",
    "    record_dict = {\n",
    "    'national_record' : {'value':row['national_record'],'col_name':'national_record_formatted'},\n",
    "    'oly_bronze_perf' : {'value':row['oly_bronze_perf'],'col_name':'oly_bronze_perf_formatted'},\n",
    "    'asiad_bronze_perf' : {'value':row['asiad_bronze_perf'],'col_name':'asiad_bronze_perf_formatted'},\n",
    "    'world_record' : {'value':row['world_record'],'col_name':'world_record_formatted'},\n",
    "    'asian_record' : {'value':row['asian_record'],'col_name':'asian_record_formatted'},\n",
    "    'asiad_gold_perf' : {'value':row['asiad_gold_perf'],'col_name':'asiad_gold_perf_formatted'}    \n",
    "    }    \n",
    "    \n",
    "    if measure_dict[event]=='time':\n",
    "        \n",
    "        for key in record_dict.keys():\n",
    "            \n",
    "            col_name = record_dict[key]['col_name']\n",
    "            \n",
    "            performance = record_dict[key]['value']\n",
    "            \n",
    "            if ((performance.count(':')== 0) and (performance.count('.')== 1)):\n",
    "                s,f = re.split('[.]',performance)\n",
    "                records_df.loc[index, col_name] = timedelta(\\\n",
    "                    hours = 0, minutes = 0, seconds = int(s), milliseconds = (int(f)*10) )\n",
    "            \n",
    "            elif ((performance.count(':')== 1) and (performance.count('.')== 1)):\n",
    "                m,s,f = re.split('[:.]',performance)\n",
    "                records_df.loc[index, col_name] = timedelta(\\\n",
    "                    hours = 0, minutes = int(m), seconds = int(s), milliseconds = (int(f)*10)) \n",
    "            \n",
    "            elif ((performance.count(':')== 2) and (performance.count('.')== 0)):\n",
    "                h,m,s = re.split('[:]',performance)\n",
    "                records_df.loc[index, col_name] = timedelta(\\\n",
    "                    hours = int(h), minutes = int(m), seconds = int(s), milliseconds = 0)\n",
    "                \n",
    "            elif ((performance.count(':')== 0) and (performance.count('.')== 0)):\n",
    "                s = performance\n",
    "                records_df.loc[index, col_name] = timedelta(\\\n",
    "                    hours = 0, minutes = 0, seconds = int(s), milliseconds = 0) \n",
    "        \n",
    "    else:\n",
    "        \n",
    "        for key in record_dict.keys():\n",
    "            \n",
    "            col_name = record_dict[key]['col_name']\n",
    "        \n",
    "            records_df.loc[index, col_name] = timedelta(\\\n",
    "                hours = 0, minutes = 0, seconds = 0, milliseconds = 0)\n",
    "            \n",
    "            \n",
    "print(\"##### DONE AND DONE ####\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cell block to calculate how old India's national records are\n",
    "\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "today = datetime.today().date()\n",
    "\n",
    "for index,row in records_df.iterrows():\n",
    "    \n",
    "    national_record_date = row['national_date']\n",
    "    \n",
    "    years_difference = relativedelta(today,national_record_date).years\n",
    "    \n",
    "    records_df.loc[index, 'national_record_age'] = years_difference\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### DONE AND DONE ###\n"
     ]
    }
   ],
   "source": [
    "#this cell block is a calculation of how far our national records are from the world records, my editor\n",
    "    #said this wouldn't make much sense, so ended up not using the resulting figures.\n",
    "\n",
    "\n",
    "for index,row in records_df.iterrows():\n",
    "    \n",
    "    event = row['event']\n",
    "    \n",
    "    if measure_dict[event]=='time':\n",
    "        \n",
    "        asian_record =  row['asian_record_formatted'].total_seconds()\n",
    "                    \n",
    "        world_record =  row['world_record_formatted'].total_seconds()\n",
    "            \n",
    "        national_record = row['national_record_formatted'].total_seconds()\n",
    "        \n",
    "        asian_record_percent = 100 - (((asian_record/world_record)*100)-100)\n",
    "        national_record_percent = 100 - (((national_record/world_record)*100)-100)\n",
    "\n",
    "        records_df.loc[index,'asian_record_percent'] = round(asian_record_percent,1)\n",
    "        records_df.loc[index,'national_record_percent'] = round(national_record_percent,1)\n",
    "        \n",
    "    else:\n",
    "        asian_record = float(row['asian_record'])\n",
    "        world_record = float(row['world_record'])\n",
    "        national_record = float(row['national_record'])\n",
    "        asian_record_percent = (asian_record/world_record)*100\n",
    "        national_record_percent = (national_record/world_record)*100\n",
    "        \n",
    "        records_df.loc[index,'asian_record_percent'] = round(asian_record_percent,1)\n",
    "        records_df.loc[index,'national_record_percent'] = round(national_record_percent,1)\n",
    "\n",
    "print('### DONE AND DONE ###')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done 2018_check/asia_men_2018_200-metres_1.html\n",
      "done 2018_check/asia_men_2018_200-metres_2.html\n",
      "done 2018_check/asia_men_2018_200-metres_3.html\n",
      "done 2018_check/asia_men_2018_200-metres_4.html\n",
      "done 2018_check/asia_men_2018_800-metres_1.html\n",
      "done 2018_check/asia_men_2018_800-metres_2.html\n",
      "done 2018_check/asia_men_2018_800-metres_3.html\n",
      "done 2018_check/asia_men_2018_1500-metres_1.html\n",
      "done 2018_check/asia_men_2018_1500-metres_2.html\n",
      "done 2018_check/asia_men_2018_5000-metres_1.html\n",
      "done 2018_check/asia_men_2018_5000-metres_2.html\n",
      "done 2018_check/asia_men_2018_5000-metres_3.html\n",
      "done 2018_check/asia_men_2018_5000-metres_4.html\n",
      "done 2018_check/asia_men_2018_5000-metres_5.html\n",
      "done 2018_check/asia_men_2018_10000-metres_1.html\n",
      "done 2018_check/asia_men_2018_10000-metres_2.html\n",
      "done 2018_check/asia_men_2018_10000-metres_3.html\n",
      "done 2018_check/asia_men_2018_400-metres-hurdles_1.html\n",
      "done 2018_check/asia_men_2018_400-metres-hurdles_2.html\n",
      "done 2018_check/asia_men_2018_3000-metres-steeplechase_1.html\n",
      "done 2018_check/asia_men_2018_long-jump_1.html\n",
      "done 2018_check/asia_men_2018_long-jump_2.html\n",
      "done 2018_check/asia_men_2018_long-jump_3.html\n",
      "done 2018_check/asia_men_2018_triple-jump_1.html\n",
      "done 2018_check/asia_men_2018_triple-jump_2.html\n",
      "done 2018_check/asia_men_2018_50-kilometres-race-walk_1.html\n",
      "done 2018_check/asia_men_2018_100-metres_1.html\n",
      "done 2018_check/asia_men_2018_100-metres_2.html\n",
      "done 2018_check/asia_men_2018_100-metres_3.html\n",
      "done 2018_check/asia_men_2018_100-metres_4.html\n",
      "done 2018_check/asia_men_2018_100-metres_5.html\n",
      "done 2018_check/asia_men_2018_400-metres_1.html\n",
      "done 2018_check/asia_men_2018_400-metres_2.html\n",
      "done 2018_check/asia_men_2018_400-metres_3.html\n",
      "done 2018_check/asia_men_2018_400-metres_4.html\n",
      "done 2018_check/asia_men_2018_high-jump_1.html\n",
      "done 2018_check/asia_men_2018_high-jump_2.html\n",
      "done 2018_check/asia_men_2018_pole-vault_1.html\n",
      "done 2018_check/asia_men_2018_pole-vault_2.html\n",
      "done 2018_check/asia_men_2018_shot-put_1.html\n",
      "done 2018_check/asia_men_2018_shot-put_2.html\n",
      "done 2018_check/asia_men_2018_discus-throw_1.html\n",
      "done 2018_check/asia_men_2018_hammer-throw_1.html\n",
      "done 2018_check/asia_men_2018_javelin-throw_1.html\n",
      "done 2018_check/asia_men_2018_javelin-throw_2.html\n",
      "done 2018_check/asia_men_2018_20-kilometres-race-walk_1.html\n",
      "done 2018_check/asia_men_2018_20-kilometres-race-walk_2.html\n",
      "done 2018_check/asia_men_2018_20-kilometres-race-walk_3.html\n",
      "done 2018_check/asia_men_2018_marathon_1.html\n",
      "done 2018_check/asia_men_2018_marathon_2.html\n",
      "done 2018_check/asia_men_2018_marathon_3.html\n",
      "done 2018_check/asia_men_2018_marathon_4.html\n",
      "done 2018_check/asia_men_2018_marathon_5.html\n",
      "done 2018_check/asia_men_2018_110-metres-hurdles_1.html\n",
      "done 2018_check/asia_men_2018_110-metres-hurdles_2.html\n",
      "done 2018_check/asia_men_2018_decathlon_1.html\n",
      "done 2018_check/asia_women_2018_1500-metres_1.html\n",
      "done 2018_check/asia_women_2018_1500-metres_2.html\n",
      "done 2018_check/asia_women_2018_5000-metres_1.html\n",
      "done 2018_check/asia_women_2018_5000-metres_2.html\n",
      "done 2018_check/asia_women_2018_5000-metres_3.html\n",
      "done 2018_check/asia_women_2018_5000-metres_4.html\n",
      "done 2018_check/asia_women_2018_10000-metres_1.html\n",
      "done 2018_check/asia_women_2018_10000-metres_2.html\n",
      "done 2018_check/asia_women_2018_400-metres-hurdles_1.html\n",
      "done 2018_check/asia_women_2018_400-metres-hurdles_2.html\n",
      "done 2018_check/asia_women_2018_long-jump_1.html\n",
      "done 2018_check/asia_women_2018_long-jump_2.html\n",
      "done 2018_check/asia_women_2018_heptathlon_1.html\n",
      "done 2018_check/asia_women_2018_discus-throw_1.html\n",
      "done 2018_check/asia_women_2018_discus-throw_2.html\n",
      "done 2018_check/asia_women_2018_100-metres_1.html\n",
      "done 2018_check/asia_women_2018_100-metres_2.html\n",
      "done 2018_check/asia_women_2018_100-metres_3.html\n",
      "done 2018_check/asia_women_2018_200-metres_1.html\n",
      "done 2018_check/asia_women_2018_200-metres_2.html\n",
      "done 2018_check/asia_women_2018_200-metres_3.html\n",
      "done 2018_check/asia_women_2018_400-metres_1.html\n",
      "done 2018_check/asia_women_2018_400-metres_2.html\n",
      "done 2018_check/asia_women_2018_400-metres_3.html\n",
      "done 2018_check/asia_women_2018_800-metres_1.html\n",
      "done 2018_check/asia_women_2018_3000-metres-steeplechase_1.html\n",
      "done 2018_check/asia_women_2018_high-jump_1.html\n",
      "done 2018_check/asia_women_2018_high-jump_2.html\n",
      "done 2018_check/asia_women_2018_pole-vault_1.html\n",
      "done 2018_check/asia_women_2018_triple-jump_1.html\n",
      "done 2018_check/asia_women_2018_triple-jump_2.html\n",
      "done 2018_check/asia_women_2018_shot-put_1.html\n",
      "done 2018_check/asia_women_2018_shot-put_2.html\n",
      "done 2018_check/asia_women_2018_hammer-throw_1.html\n",
      "done 2018_check/asia_women_2018_hammer-throw_2.html\n",
      "done 2018_check/asia_women_2018_javelin-throw_1.html\n",
      "done 2018_check/asia_women_2018_javelin-throw_2.html\n",
      "done 2018_check/asia_women_2018_20-kilometres-race-walk_1.html\n",
      "done 2018_check/asia_women_2018_marathon_1.html\n",
      "done 2018_check/asia_women_2018_marathon_2.html\n",
      "done 2018_check/asia_women_2018_marathon_3.html\n",
      "done 2018_check/asia_women_2018_marathon_4.html\n",
      "done 2018_check/asia_women_2018_100-metres-hurdles_1.html\n",
      "done 2018_check/asia_women_2018_100-metres-hurdles_2.html\n",
      "done and done\n"
     ]
    }
   ],
   "source": [
    "#here i'm scraping iaaf.org for the best performance by asian athletes for the three years 2016, 2017 and 2018\n",
    "    #so this will have the best performance by an athlete for that year\n",
    "    # these pages will also be used to create indian athlete-only dataframes\n",
    "    \n",
    "    #These pages were downloaded on August 11, 2018\n",
    "\n",
    "%reset -f\n",
    "\n",
    "import requests\n",
    "import csv\n",
    "from lxml import html\n",
    "import re\n",
    "from random import randint\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import requests\n",
    "from datetime import datetime,date\n",
    "import os\n",
    "\n",
    "genders = ['men','women']\n",
    "\n",
    "years = list(range(2016,2019)) #so 2016, 2017, 2018\n",
    "#years = [2018] used this line of code to get updated pages for asia for the year 2018\n",
    "\n",
    "\n",
    "parameter_dict = {\n",
    "    \"sprints/100-metres\":\"&timing=all&windReading=all\",\n",
    "    \"sprints/200-metres\":\"&timing=all&windReading=all\",\n",
    "    \"sprints/400-metres\":\"&timing=all\",\n",
    "    \"middlelong/800-metres\":\"&timing=all\",\n",
    "    \"hurdles/110-metres-hurdles\":\"&timing=all&windReading=all\",\n",
    "    \"hurdles/400-metres-hurdles\":\"&timing=all\",\n",
    "    \"jumps/long-jump\":\"&windReading=all\",\n",
    "    \"jumps/triple-jump\":\"&windReading=all\",\n",
    "    \"road-running/marathon\":\"&drop=all&fiftyPercentRule=all\",\n",
    "    \"combined-events/decathlon\":\"&windReading=all\",\n",
    "    \"hurdles/100-metres-hurdles\":\"&timing=all&windReading=all\",\n",
    "    \"combined-events/heptathlon\":\"&windReading=all\"\n",
    "}\n",
    "\n",
    "parameter_list = list(parameter_dict.keys())\n",
    "\n",
    "men_events = [\n",
    "    \"sprints/200-metres\",\n",
    "    \"middlelong/800-metres\",\n",
    "    \"middlelong/1500-metres\",\n",
    "    \"middlelong/5000-metres\",\n",
    "    \"middlelong/10000-metres\",\n",
    "    \"hurdles/400-metres-hurdles\",\n",
    "    \"middlelong/3000-metres-steeplechase\",\n",
    "    \"jumps/long-jump\",\n",
    "    \"jumps/triple-jump\",\n",
    "    \"race-walks/50-kilometres-race-walk\",\n",
    "    \"sprints/100-metres\",\"sprints/400-metres\",\n",
    "    \"jumps/high-jump\",\n",
    "    \"jumps/pole-vault\",\"throws/shot-put\",\"throws/discus-throw\",\n",
    "    \"throws/hammer-throw\",\"throws/javelin-throw\",\"race-walks/20-kilometres-race-walk\",\n",
    "    \"road-running/marathon\",\n",
    "    \"hurdles/110-metres-hurdles\",\"combined-events/decathlon\"\n",
    "]\n",
    "\n",
    "women_events = [\n",
    "    \"middlelong/1500-metres\",\n",
    "    \"middlelong/5000-metres\",\n",
    "    \"middlelong/10000-metres\",\n",
    "    \"hurdles/400-metres-hurdles\",\n",
    "    \"jumps/long-jump\",\n",
    "    \"combined-events/heptathlon\",\n",
    "    \"throws/discus-throw\",\n",
    "    \"sprints/100-metres\",\"sprints/200-metres\",\"sprints/400-metres\",\"middlelong/800-metres\",\n",
    "    \"middlelong/3000-metres-steeplechase\",\"jumps/high-jump\",\n",
    "    \"jumps/pole-vault\",\"jumps/triple-jump\",\"throws/shot-put\",\n",
    "    \"throws/hammer-throw\",\"throws/javelin-throw\",\"race-walks/20-kilometres-race-walk\",\n",
    "    \"road-running/marathon\",\"hurdles/100-metres-hurdles\"\n",
    "]\n",
    "\n",
    "headersx = {\n",
    "\"accept\": \"application/json, text/javascript, */*; q=0.01\",\n",
    "\"accept-encoding\": \"gzip, deflate, br\",\n",
    "\"accept-language\": \"en-US,en;q=0.9,en-GB;q=0.8\",\n",
    "\"cache-control\": \"no-cache\",\n",
    "\"cookie\": \"gsScrollPos-9386=0; gsScrollPos-10762=0; gsScrollPos-790=0; gsScrollPos-974=0; gsScrollPos-3824=; iaaf-cookie-banner=true; __utmc=94867460; gsScrollPos-10654=; __RequestVerificationToken=g-3FSrN44kRGhZiQtzyy0ctCxhalngAzukSY7O15-Iqd8kVd1Q-_E0CEhM6msCmZka99IDuXPA-ilKI_r1IlsMmPYo_rzWVlUQN1F9XZJ1w1; __utmz=94867460.1533115638.35.4.utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=(not%20provided); __utma=94867460.53648025.1530600181.1534006950.1534006950.43\",\n",
    "\"pragma\": \"no-cache\",\n",
    "\"referer\": \"https://www.iaaf.org/\",\n",
    "\"user-agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36\",\n",
    "\"x-requested-with\": \"XMLHttpRequest\"\n",
    "}\n",
    "\n",
    "for year in years:\n",
    "    for gender in genders:\n",
    "        if gender == 'men':\n",
    "            events = men_events\n",
    "        else:\n",
    "            events = women_events\n",
    "\n",
    "        for event in events:\n",
    "            if event in parameter_list:\n",
    "                parameter = \"&\" + parameter_dict[event]\n",
    "            else:\n",
    "                parameter = \"\"\n",
    "\n",
    "            combined_url = \"https://www.iaaf.org/records/toplists/\" + str(event) + \"/outdoor/\" + gender +\\\n",
    "            \"/senior/\" + str(year) +\\\n",
    "            \"?regionType=area&region=asia\" + parameter +\\\n",
    "            \"&page=1\" + \"&bestResultsOnly=true\"  \n",
    "\n",
    "            responsex = requests.get(combined_url, headers = headersx)\n",
    "\n",
    "            delay = randint(5,8)\n",
    "            time.sleep(delay)\n",
    "\n",
    "            event_string = (re.findall(r\".*/(.*)\", event))[0]  \n",
    "\n",
    "            treex = html.fromstring(responsex.content)\n",
    "\n",
    "            page_links = treex.xpath(\"//*[@id='toplists']/div[1]/div/a\")\n",
    "\n",
    "            number_page_links = len(page_links)\n",
    "\n",
    "            if number_page_links == 1:\n",
    "                pg_number_list = [1]\n",
    "\n",
    "            elif number_page_links == 4:\n",
    "                pg_number_list = [1,2]\n",
    "\n",
    "            elif number_page_links > 4:\n",
    "\n",
    "                number_pages = number_page_links - 2\n",
    "\n",
    "                pg_number_list = list(range(1, (number_pages + 1)))\n",
    "\n",
    "            for pg_number in pg_number_list:\n",
    "                \n",
    "                combined_urlz = \"https://www.iaaf.org/records/toplists/\" +\\\n",
    "                event + \"/outdoor/\" + gender + \"/senior/\" + str(year) +\\\n",
    "                \"?regionType=area&region=asia\" + parameter +\\\n",
    "                \"&page=\" + str(pg_number) + \"&bestResultsOnly=true\"  \n",
    "\n",
    "                responsez = requests.get(combined_urlz, headers = headersx)\n",
    "\n",
    "                delay = randint(5,8)\n",
    "                time.sleep(delay)\n",
    "\n",
    "                file_save_locationz =\\\n",
    "                'event_pages/asia' + '_' + gender  + '_' + str(year) + '_' + event_string + '_'  +\\\n",
    "                str(pg_number) + '.html'\n",
    "\n",
    "                with open(file_save_locationz, 'wb') as f:\n",
    "                    f.write(responsez.content)\n",
    "\n",
    "                print(\"done {}\".format(file_save_locationz))\n",
    "                    \n",
    "print('done and done')\n",
    "\n",
    "#ignore the folder name that comes below, used this code block to get updated pages for the year 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting event_pages/asia_men_2016_200-metres_1.html\n",
      "starting event_pages/asia_men_2016_800-metres_1.html\n",
      "starting event_pages/asia_men_2016_1500-metres_1.html\n",
      "starting event_pages/asia_men_2016_5000-metres_1.html\n",
      "starting event_pages/asia_men_2016_10000-metres_1.html\n",
      "starting event_pages/asia_men_2016_400-metres-hurdles_1.html\n",
      "starting event_pages/asia_men_2016_3000-metres-steeplechase_1.html\n",
      "starting event_pages/asia_men_2016_long-jump_1.html\n",
      "starting event_pages/asia_men_2016_triple-jump_1.html\n",
      "starting event_pages/asia_men_2016_50-kilometres-race-walk_1.html\n",
      "starting event_pages/asia_men_2016_100-metres_1.html\n",
      "starting event_pages/asia_men_2016_400-metres_1.html\n",
      "starting event_pages/asia_men_2016_high-jump_1.html\n",
      "starting event_pages/asia_men_2016_pole-vault_1.html\n",
      "starting event_pages/asia_men_2016_shot-put_1.html\n",
      "starting event_pages/asia_men_2016_discus-throw_1.html\n",
      "starting event_pages/asia_men_2016_hammer-throw_1.html\n",
      "starting event_pages/asia_men_2016_javelin-throw_1.html\n",
      "starting event_pages/asia_men_2016_20-kilometres-race-walk_1.html\n",
      "starting event_pages/asia_men_2016_marathon_1.html\n",
      "starting event_pages/asia_men_2016_110-metres-hurdles_1.html\n",
      "starting event_pages/asia_men_2016_decathlon_1.html\n",
      "how did we get here?\n",
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      "starting event_pages/asia_women_2016_1500-metres_1.html\n",
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      "starting event_pages/asia_women_2016_400-metres-hurdles_1.html\n",
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      "starting event_pages/asia_women_2016_heptathlon_1.html\n",
      "how did we get here?\n",
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      "starting event_pages/asia_women_2016_discus-throw_1.html\n",
      "starting event_pages/asia_women_2016_100-metres_1.html\n",
      "starting event_pages/asia_women_2016_200-metres_1.html\n",
      "starting event_pages/asia_women_2016_400-metres_1.html\n",
      "starting event_pages/asia_women_2016_800-metres_1.html\n",
      "starting event_pages/asia_women_2016_3000-metres-steeplechase_1.html\n",
      "starting event_pages/asia_women_2016_high-jump_1.html\n",
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      "starting event_pages/asia_women_2017_1500-metres_1.html\n",
      "starting event_pages/asia_women_2017_5000-metres_1.html\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "starting event_pages/asia_women_2017_discus-throw_1.html\n",
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      "starting event_pages/asia_women_2017_javelin-throw_1.html\n",
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      "starting event_pages/asia_women_2017_marathon_1.html\n",
      "starting event_pages/asia_women_2017_100-metres-hurdles_1.html\n",
      "starting event_pages/asia_men_2018_200-metres_1.html\n",
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      "starting event_pages/asia_women_2018_1500-metres_1.html\n",
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      "starting event_pages/asia_women_2018_discus-throw_1.html\n",
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      "starting event_pages/asia_women_2018_3000-metres-steeplechase_1.html\n",
      "starting event_pages/asia_women_2018_high-jump_1.html\n",
      "starting event_pages/asia_women_2018_pole-vault_1.html\n",
      "starting event_pages/asia_women_2018_triple-jump_1.html\n",
      "starting event_pages/asia_women_2018_shot-put_1.html\n",
      "starting event_pages/asia_women_2018_hammer-throw_1.html\n",
      "starting event_pages/asia_women_2018_javelin-throw_1.html\n",
      "starting event_pages/asia_women_2018_20-kilometres-race-walk_1.html\n",
      "starting event_pages/asia_women_2018_marathon_1.html\n",
      "starting event_pages/asia_women_2018_100-metres-hurdles_1.html\n",
      "\n",
      "\n",
      "#### DONE AND DONE #####\n"
     ]
    }
   ],
   "source": [
    "#used this cell block to create a csv with data scraped from all the downloaded pages\n",
    "    #so this csv will have info such as 'event','name of athlete','country','gender','performance','date' etc.\n",
    "\n",
    "\n",
    "import csv\n",
    "from lxml import html\n",
    "import re\n",
    "\n",
    "genders = ['men','women']\n",
    "years = list(range(2016,2019))\n",
    "\n",
    "men_events = [\n",
    "    \"sprints/200-metres\",\n",
    "    \"middlelong/800-metres\",\n",
    "    \"middlelong/1500-metres\",\n",
    "    \"middlelong/5000-metres\",\n",
    "    \"middlelong/10000-metres\",\n",
    "    \"hurdles/400-metres-hurdles\",\n",
    "    \"middlelong/3000-metres-steeplechase\",\n",
    "    \"jumps/long-jump\",\n",
    "    \"jumps/triple-jump\",\n",
    "    \"race-walks/50-kilometres-race-walk\",\n",
    "        \"sprints/100-metres\",\"sprints/400-metres\",\n",
    "    \"jumps/high-jump\",\n",
    "    \"jumps/pole-vault\",\"throws/shot-put\",\"throws/discus-throw\",\n",
    "    \"throws/hammer-throw\",\"throws/javelin-throw\",\"race-walks/20-kilometres-race-walk\",\n",
    "    \"road-running/marathon\",\n",
    "    \"hurdles/110-metres-hurdles\",\"combined-events/decathlon\"\n",
    "]\n",
    "\n",
    "women_events = [\n",
    "    \"middlelong/1500-metres\",\n",
    "    \"middlelong/5000-metres\",\n",
    "    \"middlelong/10000-metres\",\n",
    "    \"hurdles/400-metres-hurdles\",\n",
    "    \"jumps/long-jump\",\n",
    "    \"combined-events/heptathlon\",\n",
    "    \"throws/discus-throw\",\n",
    "    \"sprints/100-metres\",\"sprints/200-metres\",\"sprints/400-metres\",\"middlelong/800-metres\",\n",
    "    \"middlelong/3000-metres-steeplechase\",\"jumps/high-jump\",\n",
    "    \"jumps/pole-vault\",\"jumps/triple-jump\",\"throws/shot-put\",\n",
    "    \"throws/hammer-throw\",\"throws/javelin-throw\",\"race-walks/20-kilometres-race-walk\",\n",
    "    \"road-running/marathon\",\"hurdles/100-metres-hurdles\"\n",
    "]\n",
    "\n",
    "labels = ['event','name','country','gender','performance','limit','venue','date','year']\n",
    "\n",
    "with open(\"data/asia_2016_2018_lastGO.csv\", \"w\") as filex:   \n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(labels)\n",
    "    \n",
    "labelsx = ['gender','event','limit']\n",
    "\n",
    "with open(\"data/asia_limit_list_lastGO.csv\", \"w\") as filex:   \n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(labelsx)\n",
    "    \n",
    "for year in years:\n",
    "    for gender in genders:\n",
    "\n",
    "        if gender == 'men':\n",
    "            events = men_events\n",
    "        else:\n",
    "            events = women_events\n",
    "\n",
    "        for event in events:\n",
    "\n",
    "            event_string = (re.findall(r\".*/(.*)\", event))[0]\n",
    "\n",
    "            event_name = event_string.replace(\"-\", \" \").capitalize()\n",
    "\n",
    "            file_open_location =\\\n",
    "            'event_pages/asia' + '_' + gender  + '_' + str(year) + '_'  + event_string + '_1.html'\n",
    "\n",
    "            print(\"starting {}\".format(file_open_location))\n",
    "\n",
    "            with open(file_open_location) as filex:\n",
    "                sourcex = filex.read()\n",
    "                treex = html.document_fromstring(sourcex)\n",
    "\n",
    "                event_min_raw = treex.xpath(\"//*[@id='toplists']/div[2]/p/text()\")[0]\n",
    "                event_min = (re.findall(r\"Limit: (.*)\", event_min_raw))[0]\n",
    "\n",
    "                #making the list of limits here and saving it as a csv\n",
    "                if year == 2016: \n",
    "\n",
    "                    new_rowx = [gender,event_name,event_min]\n",
    "\n",
    "                    with open(\"data/asia_limit_list_lastGO.csv\", \"a\") as filez:\n",
    "                        wr = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "                        wr.writerow(new_rowx) \n",
    "\n",
    "                page_links = treex.xpath(\"//*[@id='toplists']/div[1]/div/a\")\n",
    "\n",
    "                number_page_links = len(page_links)\n",
    "\n",
    "                if number_page_links == 1:\n",
    "                    pg_number_list = [1]\n",
    "\n",
    "                elif number_page_links == 4:\n",
    "                    pg_number_list = [1,2]\n",
    "\n",
    "                elif number_page_links > 4:\n",
    "\n",
    "                    number_pages = number_page_links - 2\n",
    "\n",
    "                    pg_number_list = list(range(1, (number_pages + 1)))\n",
    "\n",
    "                for pg_number in pg_number_list:\n",
    "\n",
    "                    file_open_locationz =\\\n",
    "                    'event_pages/asia' + '_' + gender  + '_' + str(year) + '_' +\\\n",
    "                    event_string + '_'  + str(pg_number) + '.html'\n",
    "\n",
    "#                         print(\"starting {}\".format(file_open_locationz))\n",
    "\n",
    "                    with open(file_open_locationz) as filey:\n",
    "                        sourcez = filey.read()\n",
    "                        treez = html.document_fromstring(sourcez)\n",
    "\n",
    "                        if len(treez.xpath(\"//*[@id='toplists']/h2/text()\")) == 0:\n",
    "                            #the line above checks if you have 'No results found' on your page. \n",
    "                            # if length is 0, that means the string 'No results found' \n",
    "                            #is not there on page \n",
    "\n",
    "                            table_heading_cells = \\\n",
    "                                    treez.xpath(\"//*[@id='toplists']/div[3]/table/thead/tr//th\")\n",
    "\n",
    "                            increment = 1 #will be incremented below\n",
    "\n",
    "                            for cell in table_heading_cells:\n",
    "\n",
    "                                try:\n",
    "                                    cell.xpath(\"./text()\")[0]\n",
    "                                except:\n",
    "                                    continue\n",
    "                                else:\n",
    "                                    table_heading_text = cell.xpath(\"./text()\")[0]\n",
    "                                    table_heading = table_heading_text.strip()\n",
    "\n",
    "                                    if table_heading == 'Competitor':\n",
    "                                        name_position = str(increment) #will be used lower down\n",
    "                                    increment += 1\n",
    "\n",
    "                            rows = treez.xpath(\"//*[@id='toplists']/div[3]/table/tbody//tr\")\n",
    "                            #REMEMBER treez not treex\n",
    "                            \n",
    "                            for row in rows:\n",
    "\n",
    "                                first_element_nbsp = row.xpath(\"td[2][@colspan='100']\") \n",
    "                                #this gives us a list\n",
    "\n",
    "                                #if you find a tablerow where the first element is an &nbsp; element, \n",
    "                                #skip it\n",
    "                                #heptathlon and decathlon result pages have these weird \n",
    "                                #point-split tablerows\n",
    "                                #if len below is zero, that row is clear to be scraped\n",
    "\n",
    "                                if len(first_element_nbsp) == 0:\n",
    "\n",
    "                                    #find number column with heading competitor\n",
    "                                    #this column keeps changing\n",
    "                                    name_position_xpath = \".//td[\" + name_position + \"]/a/text()\"\n",
    "                                    name = row.xpath(name_position_xpath)[0]\n",
    "                                    name = name.strip()\n",
    "\n",
    "                                    #GET NATIONALITY COLUMN HERE\n",
    "                                    #DONT FORGET TO DEFINE COUNTRY HERE\n",
    "                                    country = row.xpath(\".//td[last()-4]/text()\")[1]\n",
    "                                    country = country.strip()\n",
    "\n",
    "                                    #2nd column is always mark/performance \n",
    "                                    performance = row.xpath(\".//td[2]/text()\")[0]\n",
    "                                    performance = performance.strip()\n",
    "\n",
    "                                    #2nd last column is always venue\n",
    "                                    venue = row.xpath(\".//td[last()-1]/text()\")[0]\n",
    "                                    venue = venue.strip()\n",
    "\n",
    "                                    #last column is always date\n",
    "                                    date = row.xpath(\".//td[last()]/text()\")[0]\n",
    "                                    date = date.strip()\n",
    "\n",
    "                                    new_row = [event_name,name,country,gender,performance,event_min,venue,\\\n",
    "                                               date,year]\n",
    "\n",
    "                                    with open(\"data/asia_2016_2018_lastGO.csv\", \"a\") as filez:\n",
    "                                        wr = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "                                        wr.writerow(new_row) \n",
    "\n",
    "                                else:\n",
    "                                    print('how did we get here?')\n",
    "                                    continue\n",
    "                        else:\n",
    "                            print('this is where we are')\n",
    "                            continue\n",
    "                            \n",
    "print('\\n')\n",
    "print('#### DONE AND DONE #####')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>performance</th>\n",
       "      <th>limit</th>\n",
       "      <th>venue</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Femi OGUNODE</td>\n",
       "      <td>QAT</td>\n",
       "      <td>men</td>\n",
       "      <td>20.10</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Doha (QAT)</td>\n",
       "      <td>06 MAY 2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Shota IIZUKA</td>\n",
       "      <td>JPN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.11</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Nagoya (JPN)</td>\n",
       "      <td>26 JUN 2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Salem Eid YAQOOB</td>\n",
       "      <td>BRN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.19</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Rio de Janeiro (BRA)</td>\n",
       "      <td>16 AUG 2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Abdullah Abkar MOHAMMED</td>\n",
       "      <td>KSA</td>\n",
       "      <td>men</td>\n",
       "      <td>20.29</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Walnut (USA)</td>\n",
       "      <td>15 APR 2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Kei TAKASE</td>\n",
       "      <td>JPN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.31</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Nagoya (JPN)</td>\n",
       "      <td>26 JUN 2016</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        event                     name country gender performance  limit  \\\n",
       "0  200 metres             Femi OGUNODE     QAT    men       20.10  22.40   \n",
       "1  200 metres             Shota IIZUKA     JPN    men       20.11  22.40   \n",
       "2  200 metres         Salem Eid YAQOOB     BRN    men       20.19  22.40   \n",
       "3  200 metres  Abdullah Abkar MOHAMMED     KSA    men       20.29  22.40   \n",
       "4  200 metres               Kei TAKASE     JPN    men       20.31  22.40   \n",
       "\n",
       "                  venue         date  year  \n",
       "0            Doha (QAT)  06 MAY 2016  2016  \n",
       "1          Nagoya (JPN)  26 JUN 2016  2016  \n",
       "2  Rio de Janeiro (BRA)  16 AUG 2016  2016  \n",
       "3          Walnut (USA)  15 APR 2016  2016  \n",
       "4          Nagoya (JPN)  26 JUN 2016  2016  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the csv created above into a pandas dataframe\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "asia_performances_df_lastGO = pd.read_csv(\"data/asia_2016_2018_lastGO.csv\")\n",
    "asia_performances_df_lastGO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some of the timings were recorded by hand and are prone to error. Those marks have an 'h' against it, like\n",
    "    #9.92h. Am removing rows, ie. performances that were hand timed in this cell block\n",
    "\n",
    "asia_performances_df_noH_lastGO = asia_performances_df_lastGO\\\n",
    "                            [asia_performances_df_lastGO['performance'].str.contains(\"h\") == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "#creating a column with a properly formatted YYYY-MM-DD date, ie. 2016-06-26 instead of 26 JUN 2016\n",
    "\n",
    "asia_performances_df_noH_lastGO['date_formatted'] = \\\n",
    "    pd.to_datetime(asia_performances_df_noH_lastGO['date'], format='%d %b %Y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event', 'name', 'country', 'gender', 'performance', 'limit', 'venue',\n",
       "       'date', 'year', 'date_formatted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asia_performances_df_noH_lastGO.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>performance</th>\n",
       "      <th>limit</th>\n",
       "      <th>venue</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>date_formatted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Femi OGUNODE</td>\n",
       "      <td>QAT</td>\n",
       "      <td>men</td>\n",
       "      <td>20.10</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Doha (QAT)</td>\n",
       "      <td>06 MAY 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-05-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Shota IIZUKA</td>\n",
       "      <td>JPN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.11</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Nagoya (JPN)</td>\n",
       "      <td>26 JUN 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Salem Eid YAQOOB</td>\n",
       "      <td>BRN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.19</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Rio de Janeiro (BRA)</td>\n",
       "      <td>16 AUG 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Abdullah Abkar MOHAMMED</td>\n",
       "      <td>KSA</td>\n",
       "      <td>men</td>\n",
       "      <td>20.29</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Walnut (USA)</td>\n",
       "      <td>15 APR 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200 metres</td>\n",
       "      <td>Kei TAKASE</td>\n",
       "      <td>JPN</td>\n",
       "      <td>men</td>\n",
       "      <td>20.31</td>\n",
       "      <td>22.40</td>\n",
       "      <td>Nagoya (JPN)</td>\n",
       "      <td>26 JUN 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-06-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        event                     name country gender performance  limit  \\\n",
       "0  200 metres             Femi OGUNODE     QAT    men       20.10  22.40   \n",
       "1  200 metres             Shota IIZUKA     JPN    men       20.11  22.40   \n",
       "2  200 metres         Salem Eid YAQOOB     BRN    men       20.19  22.40   \n",
       "3  200 metres  Abdullah Abkar MOHAMMED     KSA    men       20.29  22.40   \n",
       "4  200 metres               Kei TAKASE     JPN    men       20.31  22.40   \n",
       "\n",
       "                  venue         date  year date_formatted  \n",
       "0            Doha (QAT)  06 MAY 2016  2016     2016-05-06  \n",
       "1          Nagoya (JPN)  26 JUN 2016  2016     2016-06-26  \n",
       "2  Rio de Janeiro (BRA)  16 AUG 2016  2016     2016-08-16  \n",
       "3          Walnut (USA)  15 APR 2016  2016     2016-04-15  \n",
       "4          Nagoya (JPN)  26 JUN 2016  2016     2016-06-26  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asia_performances_df_noH_lastGO.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/shijith/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "29572\n",
      "29573\n",
      "29574\n",
      "29575\n",
      "29576\n",
      "29577\n",
      "29578\n",
      "29579\n",
      "29580\n",
      "29581\n",
      "29582\n",
      "29583\n",
      "29584\n",
      "29585\n",
      "29586\n",
      "29587\n",
      "29588\n",
      "29589\n",
      "29590\n",
      "29591\n",
      "29592\n",
      "29593\n",
      "29594\n",
      "29595\n",
      "29596\n",
      "29597\n",
      "29598\n",
      "29599\n",
      "29600\n",
      "29601\n",
      "29602\n",
      "29603\n",
      "29604\n",
      "29605\n",
      "29606\n",
      "29607\n",
      "29608\n",
      "29609\n",
      "##### DONE AND DONE ####\n"
     ]
    }
   ],
   "source": [
    "#creating a column where the performances are properly recognised as time duration as opposed to text strings\n",
    "\n",
    "#will be using a datatype called timedeltas for this\n",
    "\n",
    "from datetime import datetime,time\n",
    "\n",
    "for index,row in asia_performances_df_noH_lastGO.iterrows():\n",
    "    \n",
    "    print(index)\n",
    "    \n",
    "    event = row['event']\n",
    "    \n",
    "    if measure_dict[event]=='time':\n",
    "        \n",
    "        performance = row['performance']\n",
    "    \n",
    "        if ((performance.count(':')== 0) and (performance.count('.')== 1)):\n",
    "            s,f = re.split('[.]',performance)\n",
    "            asia_performances_df_noH_lastGO.loc[index,'performance_formatted'] = timedelta(\\\n",
    "                hours = 0, minutes = 0, seconds = int(s), milliseconds = (int(f)*10) )\n",
    "            \n",
    "        elif ((performance.count(':')== 1) and (performance.count('.')== 1)):\n",
    "            m,s,f = re.split('[:.]',performance)\n",
    "            asia_performances_df_noH_lastGO.loc[index,'performance_formatted'] = timedelta(\\\n",
    "                hours = 0, minutes = int(m), seconds = int(s), milliseconds = (int(f)*10))\n",
    "            \n",
    "        elif ((performance.count(':')== 2) and (performance.count('.')== 0)):\n",
    "            h,m,s = re.split('[:]',performance)\n",
    "            asia_performances_df_noH_lastGO.loc[index,'performance_formatted'] = timedelta(\\\n",
    "                hours = int(h), minutes = int(m), seconds = int(s), milliseconds = 0)\n",
    "            \n",
    "        elif ((performance.count(':')== 0) and (performance.count('.')== 0)):\n",
    "            s = performance\n",
    "            asia_performances_df_noH_lastGO.loc[index,'performance_formatted'] = timedelta(\\\n",
    "                hours = 0, minutes = 0, seconds = int(s), milliseconds = 0) \n",
    "\n",
    "    else:\n",
    "        asia_performances_df_noH_lastGO.loc[index,'performance_formatted'] = timedelta(\\\n",
    "                hours = 0, minutes = 0, seconds = 0, milliseconds = 0)\n",
    "            \n",
    "print(\"##### DONE AND DONE ####\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29262, 11)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asia_performances_df_noH_lastGO.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "#this cell block will get rid of duplicate entries in the dataframe if any\n",
    "\n",
    "asia_performances_df_noH_lastGO.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29262, 11)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asia_performances_df_noH_lastGO.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "asia_performances_df_noH_lastGO.to_csv('data/asia_2016_2018_formatted_lastGO.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "men\n",
      "200 metres\n",
      "men\n",
      "800 metres\n",
      "men\n",
      "1500 metres\n",
      "men\n",
      "5000 metres\n",
      "men\n",
      "10000 metres\n",
      "men\n",
      "400 metres hurdles\n",
      "men\n",
      "3000 metres steeplechase\n",
      "men\n",
      "Long jump\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:72: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "men\n",
      "Triple jump\n",
      "men\n",
      "50 kilometres race walk\n",
      "men\n",
      "100 metres\n",
      "men\n",
      "400 metres\n",
      "men\n",
      "High jump\n",
      "men\n",
      "Pole vault\n",
      "men\n",
      "Shot put\n",
      "men\n",
      "Discus throw\n",
      "men\n",
      "Hammer throw\n",
      "men\n",
      "Javelin throw\n",
      "men\n",
      "20 kilometres race walk\n",
      "men\n",
      "Marathon\n",
      "men\n",
      "110 metres hurdles\n",
      "men\n",
      "Decathlon\n",
      "women\n",
      "1500 metres\n",
      "women\n",
      "5000 metres\n",
      "women\n",
      "10000 metres\n",
      "women\n",
      "400 metres hurdles\n",
      "women\n",
      "Long jump\n",
      "women\n",
      "Heptathlon\n",
      "women\n",
      "Discus throw\n",
      "women\n",
      "100 metres\n",
      "women\n",
      "200 metres\n",
      "women\n",
      "400 metres\n",
      "women\n",
      "800 metres\n",
      "women\n",
      "3000 metres steeplechase\n",
      "women\n",
      "High jump\n",
      "women\n",
      "Pole vault\n",
      "women\n",
      "Triple jump\n",
      "women\n",
      "Shot put\n",
      "women\n",
      "Hammer throw\n",
      "women\n",
      "Javelin throw\n",
      "women\n",
      "20 kilometres race walk\n",
      "women\n",
      "Marathon\n",
      "women\n",
      "100 metres hurdles\n",
      "### DONE AND DONE ###\n"
     ]
    }
   ],
   "source": [
    "#what this cell block does is find the number of asian athletes in each event who have performed better\n",
    "    #than the 2014 Asian Games gold-medal winning mark in the past 2 and a half years\n",
    "    #this gives us an idea of which events have seen an improvement in Asian standards\n",
    "    #have also counted Indians for good measure\n",
    "\n",
    "men_events = list(asia_performances_df_noH_lastGO\\\n",
    "                  [asia_performances_df_noH_lastGO['gender'] == 'men']['event'].unique())\n",
    "\n",
    "women_events = list(asia_performances_df_noH_lastGO\\\n",
    "                    [asia_performances_df_noH_lastGO['gender'] == 'women']['event'].unique())\n",
    "\n",
    "genders = ['men','women']\n",
    "\n",
    "labels = ['gender','event','gold_medal_mark','asians_bettered','indians_bettered']\n",
    "\n",
    "with open(\"data/asiad_gold_bettered_lastGO.csv\", \"w\") as filex:   \n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(labels)\n",
    "\n",
    "for gender in genders:\n",
    "    if gender == 'men':\n",
    "        event_list = men_events\n",
    "    else:\n",
    "        event_list = women_events\n",
    "    \n",
    "    for event in event_list:\n",
    "        print(gender)\n",
    "        print(event)\n",
    "        \n",
    "        asia_performances_df_noH_lastGO_event = asia_performances_df_noH_lastGO\\\n",
    "                                            [(asia_performances_df_noH_lastGO['gender'] == gender)&\\\n",
    "                                             (asia_performances_df_noH_lastGO['event'] == event)]\n",
    "            \n",
    "        records_df_event = records_df[(records_df['gender'] == gender) &\\\n",
    "                              (records_df['event'] == event)]\n",
    "        \n",
    "        event_asians = 0\n",
    "        \n",
    "        event_indians = 0\n",
    "        \n",
    "        if measure_dict[event]=='time':\n",
    "            \n",
    "            gold_medal_mark = records_df_event['asiad_gold_perf'].iloc[0]\n",
    "            \n",
    "            records_df_event_asiad_performance = records_df_event['asiad_gold_perf_formatted'].iloc[0]\n",
    "            \n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed = asia_performances_df_noH_lastGO_event\\\n",
    "                                                .sort_values(by='performance_formatted',ascending=True)\n",
    "                \n",
    "            asia_performances_df_noH_lastGO_event_timed = asia_performances_df_noH_lastGO_event_timed\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            for index,row in asia_performances_df_noH_lastGO_event_timed.iterrows():\n",
    "                \n",
    "                athlete_performance = row['performance_formatted']\n",
    "                \n",
    "                athlete_country = row['country']\n",
    "                \n",
    "                if athlete_performance < records_df_event_asiad_performance:\n",
    "                    \n",
    "                    event_asians += 1\n",
    "                    \n",
    "                    if athlete_country == 'IND':\n",
    "                        \n",
    "                        event_indians += 1  \n",
    "        \n",
    "        else:\n",
    "            \n",
    "            gold_medal_mark = records_df_event['asiad_gold_perf'].iloc[0]\n",
    "            \n",
    "            records_df_event['asiad_gold_perf'] = records_df_event['asiad_gold_perf'].astype('float64')\n",
    "                \n",
    "            records_df_event_asiad_performance = records_df_event['asiad_gold_perf'].iloc[0]#MORE-HERE\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event['performance'] =\\\n",
    "                    asia_performances_df_noH_lastGO_event['performance'].astype('float64')\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other = asia_performances_df_noH_lastGO_event\\\n",
    "                                                    .sort_values(by='performance',ascending=False)\n",
    "                \n",
    "            asia_performances_df_noH_lastGO_event_other = asia_performances_df_noH_lastGO_event_other\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "\n",
    "            asia_performances_df_noH_lastGO_event_other.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            \n",
    "            for index,row in asia_performances_df_noH_lastGO_event_other.iterrows():\n",
    "                \n",
    "                athlete_performance = row['performance']\n",
    "                \n",
    "                athlete_country = row['country']\n",
    "                \n",
    "                if athlete_performance > records_df_event_asiad_performance:\n",
    "                    \n",
    "                    event_asians += 1\n",
    "                    \n",
    "                    if athlete_country == 'IND':\n",
    "                        \n",
    "                        event_indians += 1 \n",
    "        \n",
    "        new_row = [gender,event,gold_medal_mark,event_asians,event_indians]\n",
    "\n",
    "        with open(\"data/asiad_gold_bettered_lastGO.csv\", \"a\") as filez:\n",
    "            wr = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "            wr.writerow(new_row)      \n",
    "            \n",
    "            \n",
    "print('### DONE AND DONE ###')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe with performances just by Indians\n",
    "\n",
    "india_performances_df = asia_performances_df_noH_lastGO\\\n",
    "                                            [asia_performances_df_noH_lastGO['country'] == 'IND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a fresh index for the dataframe\n",
    "\n",
    "india_performances_df.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "men\n",
      "200 metres\n",
      "men\n",
      "800 metres\n",
      "men\n",
      "1500 metres\n",
      "men\n",
      "5000 metres\n",
      "men\n",
      "10000 metres\n",
      "men\n",
      "400 metres hurdles\n",
      "men\n",
      "3000 metres steeplechase\n",
      "men\n",
      "Long jump\n",
      "men\n",
      "Triple jump\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:52: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "men\n",
      "50 kilometres race walk\n",
      "men\n",
      "100 metres\n",
      "men\n",
      "400 metres\n",
      "men\n",
      "High jump\n",
      "men\n",
      "Pole vault\n",
      "men\n",
      "Shot put\n",
      "men\n",
      "Discus throw\n",
      "men\n",
      "Hammer throw\n",
      "men\n",
      "Javelin throw\n",
      "men\n",
      "20 kilometres race walk\n",
      "men\n",
      "Marathon\n",
      "men\n",
      "110 metres hurdles\n",
      "men\n",
      "Decathlon\n",
      "women\n",
      "1500 metres\n",
      "women\n",
      "5000 metres\n",
      "women\n",
      "10000 metres\n",
      "women\n",
      "400 metres hurdles\n",
      "women\n",
      "Long jump\n",
      "women\n",
      "Heptathlon\n",
      "women\n",
      "Discus throw\n",
      "women\n",
      "100 metres\n",
      "women\n",
      "200 metres\n",
      "women\n",
      "400 metres\n",
      "women\n",
      "800 metres\n",
      "women\n",
      "3000 metres steeplechase\n",
      "women\n",
      "High jump\n",
      "women\n",
      "Pole vault\n",
      "women\n",
      "Triple jump\n",
      "women\n",
      "Shot put\n",
      "women\n",
      "Hammer throw\n",
      "women\n",
      "Javelin throw\n",
      "women\n",
      "20 kilometres race walk\n",
      "women\n",
      "Marathon\n",
      "women\n",
      "100 metres hurdles\n",
      "### DONE AND DONE ###\n"
     ]
    }
   ],
   "source": [
    "#what this cell block does is find the number of indian athletes in each event who have performed better\n",
    "    #than the 2014 Asian Games bronze-medal winning mark in the past 2 and a half years\n",
    "    #Gives us an idea of how many Indians could have possibly placed on the podium at the 2014 Games\n",
    "    #Tells us what kind of a base each event has that performs at a minimum acceptable level \n",
    "                                                                    #in international athletics\n",
    "\n",
    "import csv\n",
    "from datetime import datetime,time\n",
    "\n",
    "men_events = list(india_performances_df\\\n",
    "                  [india_performances_df['gender'] == 'men']['event'].unique())\n",
    "women_events = list(india_performances_df\\\n",
    "                    [india_performances_df['gender'] == 'women']['event'].unique())\n",
    "\n",
    "genders = ['men','women']\n",
    "\n",
    "labels = ['gender','event','indians_bettered']\n",
    "\n",
    "with open(\"data/asiad_bronze_bettered_indians_lastGO.csv\", \"w\") as filex:   \n",
    "    wr = csv.writer(filex, delimiter = ',' , quotechar = '\"' )\n",
    "    wr.writerow(labels)\n",
    "\n",
    "for gender in genders:\n",
    "    if gender == 'men':\n",
    "        event_list = men_events\n",
    "    else:\n",
    "        event_list = women_events\n",
    "    \n",
    "    for event in event_list:\n",
    "        print(gender)\n",
    "        print(event)\n",
    "        \n",
    "        india_performances_df_event = india_performances_df\\\n",
    "                                            [(india_performances_df['gender'] == gender)&\\\n",
    "                                             (india_performances_df['event'] == event)]\n",
    "            \n",
    "        records_df_event = records_df[(records_df['gender'] == gender) &\\\n",
    "                              (records_df['event'] == event)]\n",
    "        \n",
    "        event_indians = 0\n",
    "        \n",
    "        if measure_dict[event]=='time':\n",
    "            \n",
    "            records_df_event_asiad_performance = records_df_event['asiad_bronze_perf_formatted'].iloc[0]\n",
    "            \n",
    "            \n",
    "            india_performances_df_event_timed = india_performances_df_event\\\n",
    "                                                .sort_values(by='performance_formatted',ascending=True)\n",
    "                \n",
    "            india_performances_df_event_timed = india_performances_df_event_timed\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "            \n",
    "            india_performances_df_event_timed.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            for index,row in india_performances_df_event_timed.iterrows():\n",
    "                \n",
    "                athlete_performance = row['performance_formatted']\n",
    "                \n",
    "                if athlete_performance < records_df_event_asiad_performance:\n",
    "                    \n",
    "                    event_indians += 1  \n",
    "        \n",
    "        else:\n",
    "            \n",
    "            records_df_event['asiad_bronze_perf'] = records_df_event['asiad_bronze_perf'].astype('float64')\n",
    "                \n",
    "            records_df_event_asiad_performance = records_df_event['asiad_bronze_perf'].iloc[0]\n",
    "            \n",
    "            india_performances_df_event['performance'] =\\\n",
    "                    india_performances_df_event['performance'].astype('float64')\n",
    "            \n",
    "            india_performances_df_event_other = india_performances_df_event\\\n",
    "                                                    .sort_values(by='performance',ascending=False)\n",
    "                \n",
    "            india_performances_df_event_other = india_performances_df_event_other\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "\n",
    "            india_performances_df_event_other.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            for index,row in india_performances_df_event_other.iterrows():\n",
    "                \n",
    "                athlete_performance = row['performance']\n",
    "                \n",
    "                if athlete_performance > records_df_event_asiad_performance:\n",
    "                    \n",
    "                    event_indians += 1 \n",
    "        \n",
    "        new_row = [gender,event,event_indians]\n",
    "\n",
    "        with open(\"data/asiad_bronze_bettered_indians_lastGO.csv\", \"a\") as filez:\n",
    "            wr = csv.writer(filez, delimiter = ',' , quotechar = '\"' )\n",
    "            wr.writerow(new_row)      \n",
    "            \n",
    "print('### DONE AND DONE ###')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 metres\n",
      "800 metres\n",
      "1500 metres\n",
      "5000 metres\n",
      "10000 metres\n",
      "400 metres hurdles\n",
      "3000 metres steeplechase\n",
      "Long jump\n",
      "Triple jump\n",
      "50 kilometres race walk\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 metres\n",
      "400 metres\n",
      "High jump\n",
      "Pole vault\n",
      "Shot put\n",
      "Discus throw\n",
      "Hammer throw\n",
      "Javelin throw\n",
      "20 kilometres race walk\n",
      "Marathon\n",
      "110 metres hurdles\n",
      "Decathlon\n",
      "1500 metres\n",
      "5000 metres\n",
      "10000 metres\n",
      "400 metres hurdles\n",
      "Long jump\n",
      "Heptathlon\n",
      "Discus throw\n",
      "100 metres\n",
      "200 metres\n",
      "400 metres\n",
      "800 metres\n",
      "3000 metres steeplechase\n",
      "High jump\n",
      "Pole vault\n",
      "Triple jump\n",
      "Shot put\n",
      "Hammer throw\n",
      "Javelin throw\n",
      "20 kilometres race walk\n",
      "Marathon\n",
      "100 metres hurdles\n",
      "### DONE AND DONE ###\n"
     ]
    }
   ],
   "source": [
    "#What this cell block is create a csv for each event showing the best performances by each Asian athlete\n",
    "    #Each of the csvs gives us an idea of how many asian athletes have done better than indian athletes\n",
    "    #if there is no two athlete per country limit\n",
    "    #(Each country can send at most two athletes for each event at the Asian Games)\n",
    "\n",
    "\n",
    "men_events = list(asia_performances_df_noH_lastGO[asia_performances_df_noH_lastGO['gender'] == 'men']['event']\\\n",
    "                                                                                                  .unique())\n",
    "women_events = list(asia_performances_df_noH_lastGO[asia_performances_df_noH_lastGO['gender'] == 'women']\\\n",
    "                                                                                        ['event'].unique())\n",
    "\n",
    "genders = ['men','women']\n",
    "\n",
    "for gender in genders:\n",
    "    if gender == 'men':\n",
    "        event_list = men_events\n",
    "    else:\n",
    "        event_list = women_events\n",
    "    \n",
    "    for event in event_list:\n",
    "        \n",
    "        print(event)\n",
    "        \n",
    "        asia_performances_df_noH_lastGO_event = asia_performances_df_noH_lastGO\\\n",
    "                                            [(asia_performances_df_noH_lastGO['gender'] == gender)&\\\n",
    "                                             (asia_performances_df_noH_lastGO['event'] == event)]\n",
    "            \n",
    "        if measure_dict[event]=='time':\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed = asia_performances_df_noH_lastGO_event.sort_values\\\n",
    "                                                                (by='performance_formatted',ascending=True)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed.reset_index(drop=True,inplace=True)\n",
    "\n",
    "            asia_performances_df_noH_lastGO_event_timed_culled = asia_performances_df_noH_lastGO_event_timed\\\n",
    "                                                                .groupby('name', as_index=False).head(1)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed_culled.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            file_path = 'data/asia_' + event + '_' + gender + '_lastGO.csv'\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_timed_culled.to_csv(file_path, index=True)\n",
    "            \n",
    "        else:\n",
    "            asia_performances_df_noH_lastGO_event['performance'] =\\\n",
    "                    asia_performances_df_noH_lastGO_event['performance'].astype('float64')\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other = asia_performances_df_noH_lastGO_event.sort_values\\\n",
    "                                                                (by='performance',ascending=False)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other_culled = asia_performances_df_noH_lastGO_event_other\\\n",
    "                                                                .groupby('name', as_index=False).head(1)\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other_culled.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            file_path = 'data/asia_' + event + '_' + gender + '_lastGO.csv'\n",
    "            \n",
    "            asia_performances_df_noH_lastGO_event_other_culled.to_csv(file_path, index=True)\n",
    "\n",
    "print(\"### DONE AND DONE ###\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 metres\n",
      "800 metres\n",
      "1500 metres\n",
      "5000 metres\n",
      "10000 metres\n",
      "400 metres hurdles\n",
      "3000 metres steeplechase\n",
      "Long jump\n",
      "Triple jump\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shijith/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:50: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 kilometres race walk\n",
      "100 metres\n",
      "400 metres\n",
      "High jump\n",
      "Pole vault\n",
      "Shot put\n",
      "Discus throw\n",
      "Hammer throw\n",
      "Javelin throw\n",
      "20 kilometres race walk\n",
      "Marathon\n",
      "110 metres hurdles\n",
      "Decathlon\n",
      "1500 metres\n",
      "5000 metres\n",
      "10000 metres\n",
      "400 metres hurdles\n",
      "Long jump\n",
      "Heptathlon\n",
      "Discus throw\n",
      "100 metres\n",
      "200 metres\n",
      "400 metres\n",
      "800 metres\n",
      "3000 metres steeplechase\n",
      "High jump\n",
      "Pole vault\n",
      "Triple jump\n",
      "Shot put\n",
      "Hammer throw\n",
      "Javelin throw\n",
      "20 kilometres race walk\n",
      "Marathon\n",
      "100 metres hurdles\n",
      "### DONE AND DONE ###\n"
     ]
    }
   ],
   "source": [
    "#this cellblock creates a csv for each event which notes down the best performances of the top two athletes\n",
    "    #for each country. Does include all Indian athletes though. \n",
    "    #Each csv tells us how many asian athletes are ahead of india athletes for medal places\n",
    "    #The numbers in the story will be different from the CSV as I looked up the Asian Games website\n",
    "        #and cut the names of those athletes who aren't there.\n",
    "\n",
    "\n",
    "men_events = list(asia_performances_df_noH_lastGO[asia_performances_df_noH_lastGO['gender'] == 'men']\\\n",
    "                                                                                        ['event'].unique())\n",
    "women_events = list(asia_performances_df_noH_lastGO[asia_performances_df_noH_lastGO['gender'] == 'women']\\\n",
    "                                                                                        ['event'].unique())\n",
    "\n",
    "genders = ['men','women']\n",
    "\n",
    "for gender in genders:\n",
    "    if gender == 'men':\n",
    "        event_list = men_events\n",
    "    else:\n",
    "        event_list = women_events\n",
    "    \n",
    "    for event in event_list:\n",
    "        \n",
    "        print(event)\n",
    "        \n",
    "        asia_performances_df_noH_event = asia_performances_df_noH_lastGO\\\n",
    "                                            [(asia_performances_df_noH_lastGO['gender'] == gender)&\\\n",
    "                                             (asia_performances_df_noH_lastGO['event'] == event)]\n",
    "            \n",
    "        if measure_dict[event]=='time':\n",
    "            \n",
    "            asia_performances_df_noH_event_timed = asia_performances_df_noH_event.sort_values\\\n",
    "                                                                (by='performance_formatted',ascending=True)\n",
    "                \n",
    "            asia_performances_df_noH_event_timed = asia_performances_df_noH_event_timed\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_noIND = asia_performances_df_noH_event_timed\\\n",
    "                            [asia_performances_df_noH_event_timed['country'] !='IND']\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_noIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_yesIND = asia_performances_df_noH_event_timed\\\n",
    "                            [asia_performances_df_noH_event_timed['country'] =='IND']\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_yesIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_noIND_top2 = asia_performances_df_noH_event_timed_noIND\\\n",
    "                                                                .groupby('country', as_index=False).head(2)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_comboIND = pd.concat\\\n",
    "                                                            ([asia_performances_df_noH_event_timed_yesIND,\n",
    "                                                              asia_performances_df_noH_event_timed_noIND_top2])\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_comboIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_comboIND_sort = \\\n",
    "                    asia_performances_df_noH_event_timed_comboIND.sort_values\\\n",
    "                                                                (by='performance_formatted',ascending=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_comboIND_sort.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            file_path = 'data/asia_' + event + '_' + gender + '_TopTwo_lastGO.csv'\n",
    "            \n",
    "            asia_performances_df_noH_event_timed_comboIND_sort.to_csv(file_path, index=True)\n",
    "            \n",
    "        else:\n",
    "            asia_performances_df_noH_event['performance'] =\\\n",
    "                    asia_performances_df_noH_event['performance'].astype('float64')\n",
    "            \n",
    "            asia_performances_df_noH_event_other = asia_performances_df_noH_event.sort_values\\\n",
    "                                                                (by='performance',ascending=False)\n",
    "                \n",
    "            asia_performances_df_noH_event_other = asia_performances_df_noH_event_other\\\n",
    "                                                    .groupby('name', as_index=False).head(1)\n",
    "\n",
    "            asia_performances_df_noH_event_other.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_noIND = asia_performances_df_noH_event_other\\\n",
    "                            [asia_performances_df_noH_event_other['country'] !='IND']\n",
    "            \n",
    "            asia_performances_df_noH_event_other_noIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_yesIND = asia_performances_df_noH_event_other\\\n",
    "                            [asia_performances_df_noH_event_other['country'] =='IND']\n",
    "            \n",
    "            asia_performances_df_noH_event_other_yesIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_noIND_top2 = asia_performances_df_noH_event_other_noIND\\\n",
    "                                                                .groupby('country', as_index=False).head(2)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_comboIND = pd.concat\\\n",
    "                                                            ([asia_performances_df_noH_event_other_yesIND,\n",
    "                                                              asia_performances_df_noH_event_other_noIND_top2])\n",
    "            \n",
    "            asia_performances_df_noH_event_other_comboIND.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_comboIND_sort = \\\n",
    "                    asia_performances_df_noH_event_other_comboIND.sort_values\\\n",
    "                                                                (by='performance',ascending=False)\n",
    "            \n",
    "            asia_performances_df_noH_event_other_comboIND_sort.reset_index(drop=True,inplace=True)\n",
    "            \n",
    "            file_path = 'data/asia_' + event + '_' + gender + '_TopTwo_lastGO.csv'\n",
    "            \n",
    "            asia_performances_df_noH_event_other_comboIND_sort.to_csv(file_path, index=True)\n",
    "\n",
    "print(\"### DONE AND DONE ###\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "records_df['national_record_timedelta'] = records_df['national_record_formatted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#just saving one of the dataframes as a csv\n",
    "\n",
    "records_df.to_csv('data/record_list_expanded.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#there were a lot of other calculations and analyses I did but you only need the code above to replicate the\n",
    "            #results in my story"
   ]
  }
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