def get_average_prices(ad_type, asset_type):
qfilter = {'ad_type': ad_type, 'asset_type': asset_type}
prices = session.execute(
'SELECT postcode, avg_price FROM inmosig_average_prices WHERE ad_type = :ad_type AND '
'asset_type = :asset_type', qfilter)
gdf = GeoDataFrame(columns=['geometry', 'price', 'postcode'])
for price in prices.fetchall():
postcode = postcode_dao.search_by_postcode(price[0])
if postcode is not None:
gdf = gdf.append(
{
'geometry': to_shape(postcode.geom),
'price': float(price[1]),
'postcode': price[0]
},
ignore_index=True)
return gdf
postcode_dao = PostcodeDAO(session)
way_dao = WayDAO(session)
# Representación de las calles con GeoSeries
# Extraemos todas las calles
ways = []
for way in way_dao.getAll():
ways.append(to_shape(way.geom))
wgs = GeoSeries(ways)
base = wgs.plot(color="blue")
qfilter = {'ad_type': 'RENT', 'asset_type': 'GARAGE'}
prices = session.execute(
'SELECT postcode, avg_price FROM inmosig_average_prices WHERE ad_type = :ad_type AND '
'asset_type = :asset_type', qfilter)
gdf = GeoDataFrame(columns=['geometry', 'price', 'postcode'])
for price in prices.fetchall():
postcode = postcode_dao.search_by_postcode(price[0])
if postcode is not None:
gdf = gdf.append(
{
'geometry': to_shape(postcode.geom),
'price': float(price[1]),
'postcode': price[0]
},
ignore_index=True)
gdf.head()
gdf.plot(ax=base, column='price', cmap='OrRd', scheme="quantiles", legend=True)
plt.show()