def proc_row(row):
for year in years:
if year not in headings: continue
val = row[headings.index(year)].strip()
if skipval(val): continue
val = float(val)
iso = wbfixes.get_iso(row[1].strip())
if iso is None: continue
# only consider aid data at country level
if iso not in isos: continue
indicator = row[3].strip()
if val > 0 and indicator in donors_iid:
src = donors_iid[indicator]
donations[year] = donations.get(year, []) + [{
'source': donors_iid[indicator],
'target': iso,
'usd': val
}]
# update donation totals for donors and recipients
countries[year][iso]['received'] = countries[year][iso].get('received', 0) + val
countries[year][src]['donated'] = countries[year][src].get('donated', 0) + val
elif indicator in indicator_ids:
countries[year][iso][indicator] = val
def proc_row(row):
for year in years:
if year not in headings: continue
val = row[headings.index(year)].strip()
if skipval(val): continue
val = float(val)
iso = wbfixes.get_iso(row[1].strip())
if iso is None: continue
# only consider aid data at country level
if iso not in isos: continue
indicator = row[3].strip()
if val > 0 and indicator in donors_iid:
src = donors_iid[indicator]
donations[year] = donations.get(
year, []) + [{
'source': donors_iid[indicator],
'target': iso,
'usd': val
}]
# update donation totals for donors and recipients
countries[year][iso]['received'] = countries[year][iso].get(
'received', 0) + val
countries[year][src]['donated'] = countries[year][src].get(
'donated', 0) + val
elif indicator in indicator_ids:
countries[year][iso][indicator] = val
# convert to strings to be able to get field position
years = [r for r in range(1990, 2013)]
# dict keyed by year containing country info
countries = {}
def get_float(val):
if '' == val:
return None
return float(val)
with open(args.csv, 'rb') as f:
r = csv.reader(f)
headings = r.next()
for row in r:
iso = wbfixes.get_iso(row[1])
if not iso: continue
indicator = row[2]
indicatorid = row[3]
region = continents[countries_by_iso[iso]['continentcode']]['name']
if iso not in countries:
countries[iso] = {
'name': row[0],
'region': region,
}
# data per year starts with column index 4
countries[iso][indicatorid] = zip(years, map(get_float, row[4:-1]))
