Hopefully you’re already convinced that you can grab whatever data you wish from webpages (and if not, later examples will hopefully tip the balance in our favour) but sooner or later you are going to ask about pdf. And let’s face it, when the latest report comes out that everyone wants to be able to […]
Let’s change gears, skip the code, and look at a harvester in action. Here’s the url for you to play with and a user-guide / description – Data Harvesting: Stock Prices From the S&P500 we choose up to 5 tickers for their daily adjusted-closes. Store those into memory with “Load Prices” against a time-frame of our […]
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Building out a fully-fledged data-harvesting bot is not our purpose here, instead we will quickly demonstrate a 101 harvester in action against a financial variable. Even though simplified, the exercise is useful: the modules of work shown below are consistent for any level of complexity. Let’s work with FX since it is straightforward. (We’ll likely look at […]
Late one evening reading Nathan Yau’s excellent “Visualize This” some years ago, I stumbled upon this undecipherable – url = ‘http://www.wunderground.com/history/airport/KBUF/2009/1/1/DailyHistory.html’ page = urllib2.urlopen(url) soup = BeautifulSoup(page) dayTemp = soup.findAll(’span’).text print dayTempurl = ‘http://www.wunderground.com/history/airport/KBUF/2009/1/1/DailyHistory.html’ page = urllib2.urlopen(url) soup = BeautifulSoup(page) dayTemp = soup.findAll(‘span’).text print dayTemp Apparently there were Pythons involved, the soup was a parser, […]