anaysis.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.1-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37164bit85b1a263971042819c2387ecb8f96bd7",
   "display_name": "Python 3.7.1 64-bit"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('results.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "AgeGroup                    object\nBikeTimeConverted           object\nFullName                    object\nGender                      object\nCountry                     object\nEventStatus                 object\nFinishRankGender             int64\nFinishRankGroup              int64\nFinishRankOverall            int64\nFinishTimeConverted         object\nRunTimeConverted            object\nSwimTimeConverted           object\nTransition1TimeConverted    object\nTransition2TimeConverted    object\ndtype: object"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>AgeGroup</th>\n      <th>BikeTimeConverted</th>\n      <th>FullName</th>\n      <th>Gender</th>\n      <th>Country</th>\n      <th>EventStatus</th>\n      <th>FinishRankGender</th>\n      <th>FinishRankGroup</th>\n      <th>FinishRankOverall</th>\n      <th>FinishTimeConverted</th>\n      <th>RunTimeConverted</th>\n      <th>SwimTimeConverted</th>\n      <th>Transition1TimeConverted</th>\n      <th>Transition2TimeConverted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>MPRO</td>\n      <td>4:16:02</td>\n      <td>Jan Frodeno</td>\n      <td>M</td>\n      <td>DE</td>\n      <td>Finish</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7:51:13</td>\n      <td>2:42:43</td>\n      <td>00:47:31</td>\n      <td>00:01:58</td>\n      <td>00:02:59</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>MPRO</td>\n      <td>4:18:11</td>\n      <td>Timothy Odonnell</td>\n      <td>M</td>\n      <td>US</td>\n      <td>Finish</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>7:59:41</td>\n      <td>2:49:45</td>\n      <td>00:47:38</td>\n      <td>00:02:01</td>\n      <td>00:02:06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>MPRO</td>\n      <td>4:15:04</td>\n      <td>Sebastian Kienle</td>\n      <td>M</td>\n      <td>DE</td>\n      <td>Finish</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>8:02:04</td>\n      <td>2:49:56</td>\n      <td>00:52:17</td>\n      <td>00:02:12</td>\n      <td>00:02:35</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>MPRO</td>\n      <td>4:24:01</td>\n      <td>Ben Hoffman</td>\n      <td>M</td>\n      <td>US</td>\n      <td>Finish</td>\n      <td>4</td>\n      <td>4</td>\n      <td>4</td>\n      <td>8:02:52</td>\n      <td>2:43:08</td>\n      <td>00:51:01</td>\n      <td>00:02:25</td>\n      <td>00:02:17</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>MPRO</td>\n      <td>4:14:44</td>\n      <td>Cameron Wurf</td>\n      <td>M</td>\n      <td>AU</td>\n      <td>Finish</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>8:06:41</td>\n      <td>2:55:03</td>\n      <td>00:52:25</td>\n      <td>00:02:07</td>\n      <td>00:02:22</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "  AgeGroup BikeTimeConverted          FullName Gender Country EventStatus  \\\n0     MPRO           4:16:02       Jan Frodeno      M      DE      Finish   \n1     MPRO           4:18:11  Timothy Odonnell      M      US      Finish   \n2     MPRO           4:15:04  Sebastian Kienle      M      DE      Finish   \n3     MPRO           4:24:01       Ben Hoffman      M      US      Finish   \n4     MPRO           4:14:44      Cameron Wurf      M      AU      Finish   \n\n   FinishRankGender  FinishRankGroup  FinishRankOverall FinishTimeConverted  \\\n0                 1                1                  1             7:51:13   \n1                 2                2                  2             7:59:41   \n2                 3                3                  3             8:02:04   \n3                 4                4                  4             8:02:52   \n4                 5                5                  5             8:06:41   \n\n  RunTimeConverted SwimTimeConverted Transition1TimeConverted  \\\n0          2:42:43          00:47:31                 00:01:58   \n1          2:49:45          00:47:38                 00:02:01   \n2          2:49:56          00:52:17                 00:02:12   \n3          2:43:08          00:51:01                 00:02:25   \n4          2:55:03          00:52:25                 00:02:07   \n\n  Transition2TimeConverted  \n0                 00:02:59  \n1                 00:02:06  \n2                 00:02:35  \n3                 00:02:17  \n4                 00:02:22  "
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "In 2019, a total of 2446 athletes participated in the Ironman World Championship\n"
    }
   ],
   "source": [
    "print(\"In 2019, a total of {0} athletes participated in the Ironman World Championship\".format(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "0.74% of said athletes did not finish the race.\n"
    }
   ],
   "source": [
    "dnf_or_dns = df[df.EventStatus != 'Finish']\n",
    "print(\"{0:3.2f}% of said athletes did not finish the race.\".format(len(dnf_or_dns)/len(df) * 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "2266\n"
    }
   ],
   "source": [
    "dnf_or_dns_indexes = dnf_or_dns.index\n",
    "df.drop(dnf_or_dns_indexes, inplace=True)\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Participants per age group')"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 576x864 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.AgeGroup.value_counts().plot(kind='barh', grid=True, figsize=(8,12))\n",
    "ax.set_title(\"Participants per age group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Participants per country')"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 576x864 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.Country.value_counts().plot(kind='barh', grid=True, figsize=(8,12))\n",
    "ax.set_title(\"Participants per country\")"
   ]
  }
 ]
}