row_dict = dict(zip(headers, row))
xref = utils.xref_from_row_dict(row_dict)
geography = utils.find_geography_by_xref(collection, xref, fields=['data'])
if not geography:
continue
if YEAR not in geography['data']:
geography['data'][YEAR] = {}
tables = {}
for k, v in row_dict.items():
# Format table names to match labels
t = utils.parse_table_from_key(k)
if t not in tables:
tables[t] = {}
tables[t][k] = v
for k, v in tables.items():
geography['data'][YEAR][k] = v
collection.update({ '_id': objectid.ObjectId(geography['_id']) }, { '$set': { 'data': geography['data'] } }, safe=True)
updates += 1
print "File: %s" % FILENAME
print ' Row count: %i' % row_count
print ' Updated: %i' % updates
continue
for k, v in geography_2000s[0]['data']['2000'][table].items():
try:
keys_2010 = KEY_MAPPINGS[k]
except KeyError:
# Skip 2000 fields that don't exist in 2010
continue
# Skip 2000 fields that don't have an equivalent in 2010
if not keys_2010:
continue
# Copy value to all 2010 fields which are comparable to this field in 2000
for key_2010 in keys_2010:
table_2010 = utils.parse_table_from_key(key_2010)
if table_2010 not in data:
data[table_2010] = {}
parts = []
for g in geography_2000s:
value = float(g['data']['2000'][table][k])
pct = geography['xwalk'][g['geoid']][crosswalk_field]
parts.append(value * pct)
data[table_2010][key_2010] = int(sum(parts))
# OTHER SUMLEVS - can be directly compared by geoid
xref = utils.xref_from_row_dict(row_dict)
geography = utils.find_geography_by_xref(collection,
xref,
fields=['data'])
if not geography:
continue
if YEAR not in geography['data']:
geography['data'][YEAR] = {}
tables = {}
for k, v in row_dict.items():
# Format table names to match labels
t = utils.parse_table_from_key(k)
if t not in tables:
tables[t] = {}
tables[t][k] = v
for k, v in tables.items():
geography['data'][YEAR][k] = v
collection.update({'_id': objectid.ObjectId(geography['_id'])},
{'$set': {
'data': geography['data']
}},
safe=True)
updates += 1
continue
for k, v in geography_2000s[0]['data']['2000'][table].items():
try:
keys_2010 = KEY_MAPPINGS[k]
except KeyError:
# Skip 2000 fields that don't exist in 2010
continue
# Skip 2000 fields that don't have an equivalent in 2010
if not keys_2010:
continue
# Copy value to all 2010 fields which are comparable to this field in 2000
for key_2010 in keys_2010:
table_2010 = utils.parse_table_from_key(key_2010)
if table_2010 not in data:
data[table_2010] = {}
parts = []
for g in geography_2000s:
value = float(g['data']['2000'][table][k])
pct = geography['xwalk'][g['geoid']][crosswalk_field]
parts.append(value * pct)
data[table_2010][key_2010] = int(sum(parts))
# OTHER SUMLEVS - can be directly compared by geoid
