1/8/2024 0 Comments Python web scraping example![]() The site is a great place to practice AJAX-data scraping. government website using Internet Explorer 6.0 in the last 90 days ![]() This script just finds the first person on the scheduled list and does some math to print the number of days until the execution (I probably made the datetime handling more convoluted than it needs to be in the provided solution) 3. But you can make it more complex by traversing the links to collect inmate data, mugshots, and final words. Texas’s death penalty site is probably one of the best places to practice web scraping, as the HTML is pretty straightforward on the main landing pages (there are several, for scheduled and past executions, and current inmate roster), which have enough interesting tabular data to collect. Number of days until Texas’s next scheduled execution But it serves as a quick and easy HTML-parsing example. This is obviously not a very robust script, as it will break when is redesigned. the 186,569 from the text string, "186,569 datasets found". I think actually has an API, but this script relies on finding the easiest tag to grab from the front page and extracting the text, i.e. But other times I solved the problem using the style in the most randomly bizarre way relative to how I would normally solve it – hey, writing 100+ scrapers gets boring.īut here are a few representative exercises with some explanation: 1. Sometimes I wrote the solution as if I were teaching it to a beginner. I didn’t sort the exercises on the list by difficulty, and many of the solutions are not particulary great code. That part is beyond what typically considered “web-scraping” and falls more into “data wrangling”. ![]() In many of these exercises, the HTML-parsing is the trivial part – just a few lines to parse the HTML to dynamically find the URL for the zip or Excel file to download (via requests)…and then 40 to 50 lines of unzipping/reading/filtering to get the answer. The exercises run the gamut of simple parsing of static HTML, to inspecting AJAX-heavy sites in which knowledge of the network panel is required to discover the JSON files to grab. The Github repo lists each exercise with the solution code, the relevant URL, and the number of lines in the solution code. And I should’ve made them do it on a weekly basis, rather than waiting till the end of the quarter to try to cram them in before finals week. ![]() round the numbers? Use complete sentences?) or even where the data files actually were – as it so happens, not everyone Googles things the same way I do. The 101 scraping exercises didn’t go so great, as I didn’t give enough specifics about what the exact answers should be (e.g. write ( data )Īnd that’s about all you need for the majority of web-scraping work – at least the part that involves reading HTML and downloading files. ![]() Url = urljoin ( base_url, href ) with open ( "/tmp/" + basename ( url ), 'wb' ) as f : print ( "Downloading", url ) # Downloading ĭata = requests. cssselect ( 'a' )] xls_hrefs = for href in xls_hrefs : print ( href ) # e.g. fromstring ( page ) hrefs = for a in doc. From os.path import basename from urllib.parse import urljoin from lxml import html import requests base_url = '' page = requests. ![]()
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