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scrape_py3.py
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import sys
try:
assert sys.version_info[0] == 3
except:
print("User does not have python3")
import requests
from bs4 import BeautifulSoup
import csv
import pandas as pd
# run as
# $ python scrape.py gender_int max_place output_filename
# read geneder_int from command line
# (gender_int = 0 for both )
# (gender_int = 1 for men )
# (gender_int = 2 for women)
gender_int = int( sys.argv[1] )
# read maximum place in any race
max_place = int( sys.argv[2] )
# output filename
output_filename = sys.argv[3]
# generic marathon guide browse url and makelinks url
browse_url = "http://www.marathonguide.com/results/browse.cfm"
makelinks_url = "http://www.marathonguide.com/results/makelinks.cfm"
# data and header objects to post to website
post_data = {
"RaceRange" : "",
"MIDD" : "",
"SubmitButton" : "View"
}
headers = {
"Referer" : "",
"User-Agent" : ("Mozilla/5.0 (Windows NT 6.1; WOW64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/34.0.1847.116 Safari/537.36")
}
# all possible fields
all_fields = [
'Last Name, First Name(Sex/Age)', 'Time',
'OverAllPlace', 'Sex Place', 'Sex Place/Div Place', 'DIV', 'Net Time',
'City, State, Country', 'State, Country', 'Country', 'City', 'City, Country',
'AG Time*', 'BQ*']
race_results = pd.DataFrame(columns = all_fields)
# read in race codes
reader = csv.DictReader(open("race_codes.csv", "rt", encoding="utf-8"))
race_codes = [ line for line in reader ]
# loop over all races
for r in range(0, len(race_codes)):
# midd and marathon url
midd = race_codes[r]["midd"]
mar_name = race_codes[r]["race"]
mar_year = race_codes[r]["year"]
mar_date = race_codes[r]["date"]
mar_url = browse_url + "?MIDD=" + midd
print(mar_name + " " + mar_year)
# update the midd and referer
post_data["MIDD"] = midd
headers["Referer"] = mar_url
# start up a session
sesh = requests.Session()
# request browse page and soup it
browse_page = sesh.get( mar_url )
browse_soup = BeautifulSoup( browse_page.content, "html5lib" )
# extract the ranges for this race
select_gender = browse_soup.find_all("select")[gender_int]
options_gender = select_gender.find_all("option")
all_race_ranges = [ options_gender[i]["value"] for i in range(1,len(options_gender)) ]
# loop over these and only keep places less than max_place
race_ranges = []
for j in range(0,len(all_race_ranges)):
rr_list = all_race_ranges[j].split(",")
if int(rr_list[1]) < max_place:
race_ranges.append( all_race_ranges[j] )
# loop over the race ranges
for j in range(0,len(race_ranges)):
race_range = race_ranges[j]
# update post data
post_data["RaceRange"] = race_range
# make post request and soup it
page = sesh.post(makelinks_url, data=post_data, headers=headers)
soup = BeautifulSoup(page.content, "html5lib")
# results sit in the 9th table
table = soup.find_all("table")[8]
# all but first three tr's are results
results_rows = table.find_all("tr")
for row in range(0,len(results_rows)):
names = results_rows[row].find_all("th")
if len(names) > 1:
field_names = [ f.get_text().strip("\n") for f in names ]
first_row = row+1
# loop over these rows
for row in range(first_row,len(results_rows)):
# extract the data fields for this row
fields = results_rows[row].find_all("td")
# insert NAs into all fields
this_result = {}
for field in all_fields:
this_result[field] = "NA"
# extract the data and add some additional race info
for f in range(0,len(fields)):
this_result[field_names[f]] = fields[f].get_text()
this_result["marathon"] = mar_name
this_result["year"] = mar_year
this_result["date"] = mar_date
this_result["midd"] = midd
race_results = race_results.append( this_result, ignore_index = True )
# rename the columns of race_results
col_names = {
'Last Name, First Name(Sex/Age)' : "name_age",
'Time' : "time",
'OverAllPlace' : "overall_place",
'Sex Place/Div Place' : "sex_div_place",
'Sex Place' : "sex_place",
'DIV' : "div",
'Net Time' : "net_time",
'City, State, Country' : "city_state_country",
'State, Country' : "state_country",
'Country' : "country",
'City' : "city",
'City, Country' : "city_country",
'AG Time*' : "ag_time",
'BQ*' : "bq"
}
race_results = race_results.rename( columns = col_names )
# write to csv without row names
race_results.to_csv(output_filename, index = False)