How to Scrape Loblaws, Superstore and No Frills Prices

Start here

Pick a banner, name what you want, give a postal code. Paste this into the Input tab of the Loblaws Scraper API on Apify, switch to JSON view, and hit Start.

{
  "banner": "superstore",
  "search_terms": ["eggs", "milk", "chicken breast"],
  "postal_code": "V5X 0C4"
}

Same run over HTTP:

curl -X POST \
  "https://api.apify.com/v2/acts/sunny_eternity~loblaws-grocery-scraper/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"banner": "superstore", "search_terms": ["eggs", "milk"], "postal_code": "V5X 0C4"}'

Swap search_terms for categories to walk a department instead, like ["fruits-vegetables", "dairy-eggs"], or ["all"] for the whole store.

What comes back

{
  "store": "Real Canadian Superstore",  "name": "Bananas",  "price": "0.69",  "unit_price": "454 g, $0.15/100g",  "comparable_unit_price": 0.15,   // normalized $/100g  "was_price": 0.89,   // pre-sale price  "is_on_sale": true,   // live sale flag  "multi_buy_deal": "2 for $1.00",   // parsed promo text  "pc_optimum_offer": "1000 points",   // loyalty points - Loblaw only  … 8 more fields}

Two of those fields are Loblaw-only. pc_optimum_offer carries loyalty points offers like "1000 points", and multi_buy_deal carries promo text like "2 for $1.00". If you’re modelling the real cost of a basket rather than the shelf price, those two are where the difference hides.

Picking the right banner

banner is the input that matters most, because it decides both the catalogue and the region. Get it wrong and you’ll scrape a chain that doesn’t operate where you’re asking about.

Postal codes resolve against Loblaw’s own store directory, the same endpoint the storefronts call to fill their store pickers, so coverage is nationwide and there’s no bundled lookup table to go stale. Every row names the store, its postal code and its coordinates. To pin one store, pass locationId instead: 1517 is Superstore Vancouver Marine Drive, 1000 is Loblaws Toronto Queen St, 7234 is Maxi Montréal Mt-Royal Ouest.

From Python

pip install apify-client, then:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("sunny_eternity/loblaws-grocery-scraper").call(run_input={
    "banner": "superstore",
    "search_terms": ["eggs", "milk", "chicken breast"],
    "postal_code": "V5X 0C4",
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["name"], item["price"], item["comparable_unit_price"], item["is_on_sale"])

From JavaScript

npm i apify-client, then:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('sunny_eternity/loblaws-grocery-scraper').call({
  banner: 'superstore',
  search_terms: ['eggs', 'milk', 'chicken breast'],
  postal_code: 'V5X 0C4',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const onSale = items.filter((i) => i.is_on_sale);

Comparing across banners

product_id is shared across the Loblaw catalogue, which is the useful bit. Run the same search_terms and region once per banner, then join on product_id and you have the same SKU priced at Superstore, No Frills and Loblaws side by side. That’s the cheapest way to see a chain’s internal price laddering.

for banner in ["superstore", "nofrills", "loblaw"]:
    run = client.actor("sunny_eternity/loblaws-grocery-scraper").call(run_input={
        "banner": banner,
        "search_terms": ["butter"],
        "postal_code": "M5H 2N2",
    })
    # join the rows on product_id afterwards

If you want that join done for you, and across Pattison and T&T as well, the comparison API handles the fan-out and the matching in a single run.

Two things that catch people

• Atlantic Superstore’s code is rass. Every other banner reads like its name. This one doesn’t, and Dominion in Newfoundland isn’t supported at all.
• comparable_unit_price is null when a size won’t parse. That’s deliberate, so a guessed number stays out of your comparison. Filter those rows out before you sort on price per 100 g.