def index():
project="realtortest"
stats = get_scrapyd_stat(project)
data = datastore.list()
[d.update({"per_sqft":float(d["price"])/float(d["sqft"])}) for d in data]
return render_template("main.html",
data=data,
job_id=get_flashed_messages(),
fields=datastore.list()[0].keys(),
**stats)
def aggregate_stats():
"""
a simple, contrived example of using our (very inefficient) datastore
to glean some insight into the data
admittedly, there's a *lot* of very inefficient work here!
"""
allstats = datastore.list() ## just get it all
p_types = set()
for stat in allstats:
p_types.add(stat["property_type"])
price_sqft = {"all":[float(p["price"])/float(p["sqft"]) for p in allstats]}
price_sqft.update({"mean":sum(price_sqft["all"])/len(price_sqft["all"]),
"med":price_sqft["all"][int(len(price_sqft["all"])/2)]})
#price_bath = {"all":[float(p["price"])/float(p["baths"]) for p in allstats]}
#price_bath.update({"mean":sum(price_bath["all"])/len(price_bath["all"]),
#"med":price_bath["all"][int(len(price_bath["all"])/2)]})
return {"types":list(p_types),
"price_sqft":price_sqft,
#"price_bed":price_bed,
#"price_bath":price_bath,
}
