def _import_gsp_file(self, input_file, year_span='2011-12'):
reader = csv.DictReader(input_file)
headers = reader.next()
for row in reader:
#we only care about the GSP (Gross State Product)
#which is mislabeled as GSDP
if not row['Sector'] == 'GSDP (2004-05 Prices)':
continue
state_name = util.clean_state_name(row['State Name'])
gsp = row[year_span]
#add the gsp information to the relevant State
for state in self.states:
if state.name == state_name and state.classification == 'total':
state.gsp = gsp
def _import_mpce_file(self, input_file, mpce_type, classification):
#http://www.blog.pythonlibrary.org/2014/02/26/python-101-reading-and-writing-csv-files/
reader = csv.reader(input_file)
for row in reader:
#The first row is just the headers, so we skip it
if row[0] == 'state':
continue
#remove extra spaces around each element in the row
row = [value.strip() for value in row]
row[0] = util.clean_state_name(row[0])
#Create a Mpce object - makes things easier to insert
#This is not a very efficient method, but it works
mpce = Mpce(mpce_type, classification, *row)
#add the row to the mpce table
insert = Mpce.__table__.insert()
self.connection.execute(insert, mpce.__dict__)