How I Built a Google Shopping Scraper with Python & Playwright
The article discusses the creation of a Google Shopping scraper using Python and Playwright. The scraper is designed to compare prices across various sellers quickly and efficiently. It extracts key product information and saves the output in JSON and CSV formats.
- ▪The scraper searches Google Shopping for any product and extracts relevant details.
- ▪It works across multiple sellers including Flipkart, Amazon, and Croma.
- ▪The output is automatically saved in both JSON and CSV formats.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/partha_singh_448b3a8240aa/how-i-built-a-google-shopping-scraper-with-python-playwright-nfn |
| Publication time | Sat, 30 May 2026 14:07:56 +0000 |
| Retrieval time | 2026-05-30T14:29:38.588Z |
| Last seen | 2026-05-30T14:29:38.588Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ScKSjzlqAxMC |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3960049) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Partha Singh Posted on May 30 How I Built a Google Shopping Scraper with Python & Playwright #python #playwright #webscraping #showdev Why I Built This I wanted to compare prices across Google Shopping without clicking through 100 tabs manually. So I built a scraper that does it in seconds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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