Stats

Products

Top products with prices

unique_scans_n

Date	Flavor	Top 100	Top 1000
2024-08-24	OFF	80	567
2024-09-13	OFF	81	583

LIMIT = 100
queryset = Product.objects.order_by("-unique_scans_n")[:LIMIT].values_list("price_count", flat=True)
len(list(filter(lambda pc: pc != 0, list(queryset))))

Number of products with prices per flavor

Date	OFF	OPF	OBF	OPPF	unknown
2024-09-13	16046 / 3390281	201 / 15618	157 / 30634	33 / 9713	2476 / 4441

from open_prices.products import constants as product_constants
for flavor in product_constants.SOURCE_LIST + [None]:
    print(flavor, Product.objects.filter(source=flavor).filter(price_count__gte=1).count(), "/", Product.objects.filter(source=flavor).count())

Top location OSM types

from collections import Counter
from django.db.models import F, Value, CharField
from django.db.models.functions import Concat

l = Location.objects.annotate(osm_tag=Concat(F("osm_tag_key"), Value(":"), F("osm_tag_value"), output_field=CharField())).values_list("osm_tag", flat=True)

Counter(l)

// top 30, stats from 08/09/2024 (961 locations)

'shop:supermarket': 614,
'shop:convenience': 76,
'boundary:administrative': 25,
'amenity:fuel': 18,
'shop:chemist': 13,
'shop:variety_store': 13,
'place:house': 7,
'highway:bus_stop': 7,
'shop:bakery': 7,
'shop:mall': 7,
'shop:deli': 7,
'shop:frozen_food': 6,
'landuse:retail': 6,
'amenity:pharmacy': 6,
'railway:station': 6,
'landuse:construction': 5,
'shop:greengrocer': 5,
'shop:furniture': 5,
'landuse:industrial': 4,
'shop:department_store': 4,
'shop:farm': 4,
'building:yes': 4,
'shop:wholesale': 4,
'shop:books': 3,
'amenity:fast_food': 3,
'shop:sports': 3,
'place:city': 3,
'shop:doityourself': 3,
'amenity:cafe': 3,
'place:suburb': 3
...