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"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"2013 Chicago Marathon Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With close to 39,000 results, the [2013 Chicago Marathon Results](http://results.chicagomarathon.com/2013/) combine two of my favourite topics, statistics and running. I decided to take this opportunity to learn more about [pandas](http://pandas.pydata.org/) by using it to analyze the result set to provide some insight into how people run marathons.\n",
"\n",
"Let me begin first by saying I am **not** a statistician and this won't be a formal or rigourous statistical analysis. Instead, these are at best Malcolm Gladwell-esque observations, less the interesting storytelling. I will be cherry-picking results in order to illustrate something that may be \"interesting\", but will likely have no statistical significance."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plotting options.\n",
"pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
"figsize(15, 5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# These results were obtained by scraping the official results site.\n",
"results = pd.read_csv('chicago_marathon_results.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Massage the data: Change the M-15/W-15 to just 0-15 to match the other division nomenclature, which does not have a gender.\n",
"results.division[results.division == 'M-15'] = '0-15'\n",
"results.division[results.division == 'W-15'] = '0-15'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what a sample result looks like, using my own result as an example: (Note that the DataFrame index is **not** the finishing place, because I had to scrape male and female results separately)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results[results.name_location == 'Chng, Peter (CAN)']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" place | \n",
" place_gender | \n",
" place_division | \n",
" name_location | \n",
" city_state | \n",
" bib | \n",
" division | \n",
" age | \n",
" half_split | \n",
" finish | \n",
" gender | \n",
" finish_seconds | \n",
" half_split_seconds | \n",
" split_diff_seconds | \n",
" split_diff | \n",
"
\n",
" \n",
" \n",
" \n",
" | 512 | \n",
" 570 | \n",
" 513 | \n",
" 153 | \n",
" Chng, Peter (CAN) | \n",
" Toronto, ON | \n",
" 1032 | \n",
" 25-29 | \n",
" 29 | \n",
" 01:27:14 | \n",
" 02:55:22 | \n",
" M | \n",
" 10522 | \n",
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" percentiles | \n",
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" | 0 | \n",
" 02:50:20 | \n",
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