When asking for and parsing data from a source with unfamiliar properties and random behavior (quite simply, scraping), All types are expected by me of bizarrities to occur. Managing exceptions are specially helpful in such cases. Here’s some techniques, an exception might be raised. Catching the exception is sometimes cleaner than preventing it from happening in the first place. Below are a few examples handling bizarre exceptions in scrapers. Let’s say we’re parsing dates. This doesn’t raise an error.
It raises a ValueError because the day forms don’t match. So what do we do if we’re scraping a data source with multiple time formats? A straightforward thing is to disregard the date formats that people didn’t expect. If we make a clean day column in a data source and put this within, we’ll involve some rows with times plus some rows with nulls. If there are only a few nulls, we would just parse those by hand. Maybe we have determined that particular databases use three different date formats.
We can try all three. This loops through three different date formats and comes back the first one which doesn’t raise the mistake. If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying later might help again. This function tries to download the page thee times.
- We launch the marketing campaign and monitor public media to engage and respond as buzz is generated
- Hiring & Vetting
- What’s Your “Why?”
- Your First Reaction on My First Look
On the first two fails, it waits 42 secs and again tries. On the third failure, the mistake is raised because of it. On a success, it returns the content of the page. For more complicated parses, you may find loads of mistakes showing up in strange places, so you may want to go through all of the documents before making a decision which to fix first or whether to do some of them personally. This attracts any exception raised by a particular document, stores it in the data source, and proceeds with another record then.
Looking at the database afterwards, you might notice some developments in the mistakes that you can simply fix plus some others where you might hard-code the correct parse. When I’m scraping over 9000 webpages and my script fails on web page 8765, I love to be able to job application where I remain off.
I can often figure out where I still left off predicated on the previous row that is saved to a data source or file, but I can’t sometimes once I don’t have a unique index especially. This will tell me which bar I left off on. It’s fancier if I save the given information to the data source, so here is how I might do that with ScraperWiki. ScraperWiki has a limit on CPU time, so one that concerns me is the scraperwiki often. CPUTimeExceededError. This mistake is raised after the script has used 80 secs of CPU time; if the exception is caught by you, you have two CPU mere seconds to clean up. You might like to handle this mistake in a different way from other mistakes.