You have two questions rolled into one, so here is a partial answer to start you off. The first task concerns HTML parsing, so let's use the python libraries: requests, and beautifulsoup4 (pip install beautifulsoup4 in case you haven't already).
import requests
from bs4 import BeautifulSoup
r = requests.get('http://www.tamswithmark.com/shows/anything-goes-beaumont-1987/')
soup = BeautifulSoup(r.content, 'html.parser')
rows = soup.findAll('tr', {"class": "upcoming_performance"})
soup is navigable data structure of the page content. We use the findAll method on soup to extract the 'tr' elements with class 'upcoming_performance'. A single element in rows looks like:
print(rows[0]) # debug statement to examine the content
"""
<tr class="upcoming_performance" data-lat="47.6007" data-lng="-120.655" data-zip="98826">
<td class="table-margin"></td>
<td class="performance_organization">Leavenworth Summer Theater</td>
<td class="performance_city-state">LEAVENWORTH, WA</td>
<td class="performance_date-from">07/15/2015</td>
<td class="performance_date_to">08/28/2015</td>
<td class="table-margin"></td>
</tr>
"""
Now, let's extract the data from these rows into our own data structure. For each row, we will create a dictionary for that performance.
The data-* attributes of each tr element are available through dictionary key lookup.
The 'td' elements inside each tr element can be accessed using the .children (or .contents) attribute.
performances = [] # list of dicts, one per performance
for tr in rows:
# extract the data-* using dictionary key lookup on tr
p = dict(
lat=float(tr['data-lat']),
lng=float(tr['data-lng']),
zipcode=tr['data-zip']
)
# extract the td children into a list called tds
tds = [child for child in tr.children if child != "\n"]
# the class of each td indicates what type of content it holds
for td in tds:
key = td['class'][0] # get first element of class list
p[key] = td.string # get the string inside the td tag
# add to our list of performances
performances.append(p)
At this point, we have a list of dictionaries in performances. The keys in each dict are:
lat : float
lng: float
zipcode: str
performance_city-state: str
performance_organization: str
etc
HTML extraction is done. Your next step is use a mapping API service that compares the distance from your desired location to the lat/lng values in performances. For example, you may choose to use Google Maps geocoding API. There are plenty of existing answered questions on SO to guide you.
