In [1]:

    

import numpy as np, pandas as pd, os
import psycopg2
import pandas.io.sql as sql
conn_string = "host='localhost' port=5432 dbname='drcog_backup' user='postgres' password='postgres'"
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
query = '''
select a.parcel_id, a.county_id, a.parcel_sqft, a.land_value, a.zone_id, a.lu_type_id, a.centroid_x, a.centroid_y, a.tax_exempt_flag, a.school_district, a.zoning_id, round(ST_X(b.centroid_geom)) as x, round(ST_Y(b.centroid_geom)) as y,
b.dist_bus, b.dist_rail, b.in_ugb, b.in_uga, b.env_constr_park, b.env_constr_lake, b.env_constr_floodplain, b.env_constr_river, b.env_constr_landslide, far_id from parcels a join parcels_spatial b on a.parcel_id = b.parcel_id
'''
parcels = sql.read_frame(query,conn)


    

In [2]:

    

parcels[['x','y']].describe()


    

    
Out[2]:






  

    

      

      
x
      
y
    
  
  

    

      
count
      
  949484.000000
      
  949484.000000
    
    

      
mean
      
 3145177.076667
      
 1700240.999813
    
    

      
std
      
   55332.772747
      
   71084.681992
    
    

      
min
      
 2887889.000000
      
 1472248.000000
    
    

      
25%
      
 3114749.000000
      
 1652022.000000
    
    

      
50%
      
 3145977.000000
      
 1692499.000000
    
    

      
75%
      
 3179069.000000
      
 1748392.000000
    
    

      
max
      
 3503749.000000
      
 1886418.000000
    
  

In [3]:

    

parcels['prop_constrained'] = parcels.env_constr_park + parcels.env_constr_lake + parcels.env_constr_floodplain + parcels.env_constr_river + parcels.env_constr_landslide


    

In [4]:

    

parcels.prop_constrained[parcels.prop_constrained>1.0] = 1.0


    

In [5]:

    

store = pd.HDFStore('c:\\urbansim\\data\\drcog.h5')
store['parcels'] = parcels