# Group errors by zipcodes, sort the zipcodes by error in descending order
import numpy as np
from pythonql.helpers import print_table
from model_eval import dataset,model1
mean = np.mean( [select d.price for d in dataset ])
std = np.std( [select d.price for d in dataset ])
res = [ select (zipcode, n_points, n_errors)
for d in dataset, m in model1
where d.record_id == m.record_id
let err = abs(d.price - m.pred) > std
group by d.zipcode as zipcode
let n_errors = sum(err),
n_points = len(err)
order by n_errors/n_points desc ]
print_table(res)
Welcome to the PythonQL Web Demo
The Demo is organized into a number of scenarios that demonstrate the power and usability of PythonQL.
Each scenario illustrates a specific use case in data science that is addressed by PythonQL.
