Comma-separated value files are a quite commonly used text form of spreadsheet. To process them often requires special cases, such as parsing dates or removing bad rows.
For more details, read the full Python CSV documentation.
Download the CSV file first. (If there are quirks in the input file, you might at this point want to preprocess the data using, for example, the .replace function)
import scraperwiki data = scraperwiki.scrape("http://s3-eu-west-1.amazonaws.com/ukhmgdata-cabinetoffice/Spend-data-2010-11-01/Spend-Transactions-with-descriptions-HMT-09-Sep-2010.csv")
Load it into the standard Python CSV reader. It needs to be a list of lines.
import csv reader = csv.reader(data.splitlines())
You can then loop through the rows as if they were a list.
for row in reader: print "£%s spent on %s" % (row, row)
Saving to the datastore
Conventionally the first line gives the names for the columns. You can get the standard reader to load in each row as a dictionary, where the keys are those names.
reader = csv.DictReader(data.splitlines())
This makes it easy to save the data. By default everything comes out as strings. We convert the 'Amount' row to a number type, so that it can then be added and sorted.
for row in reader: if row['Transaction Number']: row['Amount'] = float(row['Amount']) scraperwiki.sqlite.save(unique_keys=['Transaction Number', 'Expense Type', 'Expense Area'], data=row)