<jupyter_start><jupyter_text>Import libraries you'll need<jupyter_code>from urbansim_templates import modelmanager as mm
from urbansim_templates.models import LargeMultinomialLogitStep
import orca
import os; os.chdir('../')
import warnings; warnings.simplefilter('ignore')<jupyter_output><empty_output><jupyter_text>Load data and pre-defined model steps<jupyter_code>from scripts import datasources
from scripts import models
from scripts import variables<jupyter_output><empty_output><jupyter_text>Generate accessibility variables<jupyter_code>orca.run(['initialize_network_small', 'network_aggregations_small'])
orca.run(['initialize_network_walk', 'network_aggregations_walk'])<jupyter_output>Running step 'initialize_network_walk'
Time to execute step 'initialize_network_walk': 0.00 s
Running step 'network_aggregations_walk'
Computing accessibility variables
Computing units_500_walk
Computing sqft_unit_500_walk
Computing singles_500_walk
Removed 52084 rows because they contain missing values
Computing elderly_hh_500_walk
Removed 34553 rows because they contain missing values
Computing children_500_walk
Removed 189769 rows because they contain missing values
Computing units_sf_500_walk
Computing units_mf_500_walk
Computing pop_500_walk
Removed 189769 rows because they contain missing values
Computing hh_500_walk
Removed 189769 rows because they contain missing values
Computing poor_500_walk
Removed 53114 rows because they contain missing values
Computing rich_500_walk
Removed 38964 rows because they contain missing values
Computing renters_500_walk
Removed 102597 rows because they contain missing values
Computing avg_income_500_walk
Removed 189769 rows because they contain m[...]<jupyter_text>List the tables and columns you can now use for model estimation<jupyter_code>for table_name in orca.list_tables():
    print(table_name.upper())
    print(orca.get_table(table_name).to_frame().columns.tolist())
    print()<jupyter_output>PARCELS
['development_type_id', 'land_value', 'acres', 'county_id', 'zone_id', 'proportion_undevelopable', 'tax_exempt_status', 'apn', 'parcel_id_local', 'geom_id', 'imputation_flag', 'x', 'y', 'shape_area', 'block_id', 'node_id', 'node_id_small', 'node_id_walk']

BUILDINGS
['parcel_id', 'development_type_id', 'improvement_value', 'residential_units', 'residential_sqft', 'sqft_per_unit', 'non_residential_sqft', 'building_sqft', 'nonres_rent_per_sqft', 'res_price_per_sqft', 'stories', 'year_built', 'redfin_sale_price', 'redfin_sale_year', 'redfin_home_type', 'costar_property_type', 'costar_rent', 'building_type_id', 'res_sqft_per_unit', 'node_id_small', 'node_id_walk']

CRAIGSLIST
['date', 'region', 'neighborhood', 'rent', 'bedrooms', 'sqft', 'rent_sqft', 'longitude', 'latitude', 'county', 'fips_block', 'state', 'bathrooms']

RENTALS
['Unnamed: 0', 'date', 'region', 'neighborhood', 'rent', 'bedrooms', 'sqft', 'rent_sqft', 'longitude', 'latitude', 'county', 'fips_block', 'state', 'bathro[...]<jupyter_text>List the pre-defined merge relationships between tables<jupyter_code>orca.list_broadcasts()<jupyter_output><empty_output><jupyter_text>View the contents of a single table<jupyter_code>orca.get_table('buildings').to_frame().describe()<jupyter_output><empty_output>
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