--- source_url: "https://dataflirt.com/scraper/depop/?utm_source=openai" title: "Depop Scraper — Resale Listings, Seller & Trend Data Extraction | DataFlirt" mirrored_at: 2026-08-09T03:01:41.235Z host: dataflirt.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/dataflirt.com/scraper/depop/index__q__utm_source_openai" --- > **Original source:** https://dataflirt.com/scraper/depop/?utm_source=openai SYSTEM **all green** source **depop.com** queue **19,482 pages** p99 latency **158ms** dataflirt.com · scraper/depop-com [Home](https://dataflirt.com/)[Scraper](https://dataflirt.com/scraper/)Depop Scraper — Resale Listings, Seller & Trend Data Extraction RUN · 83 active pipelines · depop.com live ## Depop data, at warehouse scale. We extract fashion resale listings, pricing signals, seller profiles, likes and demand signals, brand and style tags, and keyword rankings from Depop. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence. Listings extracted 740K /day Price updates 3.2M /24h Seller records 210K /run Active pipelines 83 Uptime 99.93% Complete list of extractable fields for **Active Listings** objects from depop.com. All fields typed and schema-versioned. listing\_idtitledescriptionseller\_idseller\_usernameseller\_followersbrandcategorysizeconditioncolorstyle\_tagsaesthetic\_tagspriceoriginal\_pricecurrencylikes\_countviews\_countships\_fromshipping\_costfree\_shippingimage\_urlslisting\_urllisted\_at active\_listings ● 200 OK "listing\_id": "dep\_184729301", "title": "Vintage Levi's 501 High Waisted Mom Jeans W28 L30", "seller\_username": "vintagevaultldn", "brand": "Levi's", "size": "W28 L30", "condition": "Good", "price": 42.00, "currency": "GBP", "likes\_count": 284, "style\_tags": \["Y2K", "Vintage", "Denim"\] # listing\_id title description seller\_id seller\_username seller\_followers 1 2 3 Complete list of extractable fields for **Sold Listings** objects from depop.com. All fields typed and schema-versioned. listing\_idtitlebrandcategorysizeconditioncolorsold\_pricecurrencyoriginal\_ask\_pricediscount\_from\_asklikes\_at\_saleseller\_idships\_fromsold\_datestyle\_tagsaesthetic\_tags sold\_listings ● 200 OK "listing\_id": "dep\_184729301", "title": "Vintage Levi's 501 High Waisted Mom Jeans W28 L30", "sold\_price": 38.00, "original\_ask\_price": 42.00, "discount\_from\_ask": 9.5, "likes\_at\_sale": 284, "condition": "Good", "sold\_date": "2026-05-08" # listing\_id title brand category size condition 1 2 3 Complete list of extractable fields for **Seller Profiles** objects from depop.com. All fields typed and schema-versioned. seller\_idusernamedisplay\_namefollowers\_countfollowing\_countlistings\_countsold\_countratingreview\_countverifiedships\_frombiotop\_brandsshop\_urlmember\_since seller\_profiles ● 200 OK "seller\_id": "vintagevaultldn", "username": "vintagevaultldn", "followers\_count": 18420, "listings\_count": 412, "sold\_count": 2841, "rating": 4.9, "top\_brands": \["Levi's", "Nike", "Carhartt"\], "member\_since": "2019-03-12" # seller\_id username display\_name followers\_count following\_count listings\_count 1 2 3 Complete list of extractable fields for **Search & Trending** objects from depop.com. All fields typed and schema-versioned. keywordpositionlisting\_idtitlebrandpricecurrencyconditionsizelikes\_countseller\_followersstyle\_tagsis\_soldthumbnail\_urlscraped\_at search\_& trending ● 200 OK "keyword": "vintage levi's jeans", "position": 1, "listing\_id": "dep\_184729301", "likes\_count": 284, "seller\_followers": 18420, "style\_tags": \["Y2K", "Vintage"\], "is\_sold": false, "scraped\_at": "2026-05-12T08:30:00Z" # keyword position listing\_id title brand price 1 2 3 * * * Capabilities ## Everything you need from Depop — nothing you don't Depop is a social-first fashion resale platform where trend signals live in likes, aesthetic tags, and seller follower counts — not just price. Our scraper captures all of it: active and sold listings, brand intelligence, style taxonomy, and seller follower dynamics. Likes as Demand Signals Capture likes counts per listing — Depop's primary demand-proxy metric. High-like, unsold listings reveal price resistance; high-like, sold listings reveal true market-clearing prices. Sold Price Extraction Scrape sold listing prices, original ask prices, and the spread between ask and sell — giving you real secondary market transaction prices, not just listing aspirations. Brand & Style Tag Intelligence Extract brand tags, style tags (Y2K, Vintage, Cottagecore, Streetwear), and aesthetic labels per listing — the trend taxonomy that defines Depop's fashion data. Size & Condition Data Full size label, condition grade (New, Like New, Good, Fair), and colour per listing — enabling size-curve and condition-pricing analysis at scale. Seller Follower Intelligence Follower count, following count, total listings, total sold count, review rating, and top brands per seller — the influence-layer data that drives Depop purchase behaviour. Search Rank & Keyword Tracking Monitor listing position for any brand, style, or keyword search on Depop — capturing likes, condition, size, and price in each result record. Listing Age & Velocity Capture listing date and correlate with likes velocity — identifying items accumulating demand rapidly, a leading indicator of sell-through before the algorithm surfaces them. Multi-Currency Support Depop listings span GBP, USD, EUR, and AUD sellers. We normalise currency per listing and apply FX conversion to a target currency on delivery. Scheduled + Streaming Modes One-off resale market snapshots or continuous trending-style monitoring pipelines at daily cadences with change-detection diffing. // engagement pipeline ## From listing ID to warehouse record Brief in. Clean data out. Define Scope d 0 Provide brand terms, style keywords, category paths, or seller usernames. We design the extraction schema together — active listings, sold listings, or both. Pipeline Build d 2–4 We configure Scrapy / Playwright crawlers, proxy rotation, session management, and Depop-specific pagination handling. Validation & QA d 4–6 Likes count null-rate audits, sold price completeness checks, brand tag coverage validation, and sample records before full launch. Delivery ongoing JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence. Under the hood ## How our Depop pipeline handles the hard parts Depop's social-first architecture, infinite scroll feeds, and likes-as-signal data model require scraping logic that goes well beyond standard e-commerce extraction. pipeline-monitor · depop.com · live ● active // fingerprinting Identity rotation TLS fingerprintrandomised User-agentrotated IP poolresidential Challenges blocked0 // pagination Page coverage 48,291 pages queued running // observability Pipeline health 99.9% uptime 142ms p99 lat 0.3% null rate 2 alerts 01 Resale Market Pricing & Valuation Resale platforms, authentication services, and fashion brands use Depop sold prices to model secondary market valuations for specific brands, items, and condition grades. 02 Fashion Trend Forecasting Fashion forecasters and brand strategy teams use Depop style tags, aesthetic labels, and likes-velocity data as a leading indicator of Gen-Z trend cycles — often 6–12 months ahead of mainstream retail adoption. 03 Brand Resale Value Intelligence Luxury and streetwear brands track their own secondary market prices, condition distribution, and likes signals on Depop to understand brand equity and resale desirability. 04 AI Training Data ML teams building fashion AI use Depop listing data — brand tags, style labels, condition grades, size data, and image URLs — as training data for fashion classification, recommendation, and resale price prediction models. 05 Seller & Influence Mapping Resale aggregators and fashion apps map high-follower Depop sellers and their top brands — identifying influential curators driving demand in specific style niches. 06 Circular Fashion Research Sustainability researchers and consultancies use Depop sold volume data, condition distribution, and category mix to quantify resale market activity and circular fashion flows. Why DataFlirt "Depop's likes count is the fashion resale market's most honest demand signal — and its sold listing prices are the only source of real Gen-Z secondary market transaction data. Neither is available via API." Extracting Depop data reliably requires infinite scroll handling, likes signal capture from listing pages, sold-vs-active listing classification, style tag extraction, and daily selector maintenance. DataFlirt absorbs that complexity so your trend research and brand strategy teams can focus on the insights — not the infrastructure. Technical Spec ## Depop scraper — technical capabilities Everything supported by our depop.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond. JavaScript rendering Full Playwright sessions — required for Depop's React SPA listing pages and seller shops Supported CAPTCHA bypass Automated 2Captcha + CapSolver integration with fallback to manual queue Supported Residential proxy rotation ISP-grade UK/US residential IPs — rotated per request Supported Likes count capture Per-listing likes count — Depop's primary demand-proxy signal, unavailable via API Supported Sold listing mining Sold price, original ask, discount from ask, and likes-at-sale for completed transactions Supported Style & aesthetic tag extraction Full style tag and aesthetic label set per listing — Y2K, Vintage, Grunge, and more Supported Seller follower intelligence Follower count, sold count, rating, and top brands per seller profile Supported Infinite scroll pagination Full scroll-based feed pagination for search results and seller shop pages Supported Brand tag extraction Primary brand tag per listing as entered by seller Supported Multi-currency capture GBP, USD, EUR, AUD with FX conversion to target currency on delivery Supported Change detection (diffs) Hash-based diff: only emit records with changed fields since last run Supported Depop account-gated data Message threads, purchase history, and private seller financials require credentials Partial Infrastructure ## Infrastructure powering the Depop pipeline Open-source tooling on proven cloud infra — no vendor lock-in, full observability. ScrapyPlaywrightPython 3.12RedisPostgreSQLApache AirflowAWS LambdaS3CloudWatch2CaptchaCapSolverResidential ProxiesDockerKubernetesGrafanaPrometheus Scrapy + Playwright Stack Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles Depop's React SPA rendering, infinite scroll triggering, and seller shop pagination. Residential Proxy Infrastructure We maintain pools of UK and US ISP residential proxies — the primary Depop user geographies. Rotation happens per-request with sticky sessions where required. Cloud-Native Orchestration Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres. Output & Delivery ## Your data, your destination Data delivered to where your team already works — no new tooling required. JSON Newline-delimited or nested — schema versioned per run CSV Flat file with typed columns — Excel/Sheets compatible Parquet Columnar format for BigQuery, Snowflake, Athena S3 Direct bucket delivery — compatible with any data lake BigQuery Streamed directly into your dataset with schema auto-detect Webhook HTTP POST per record for real-time downstream processing Postgres Upsert into your existing schema with conflict resolution Snowflake Stage + COPY INTO workflow — incremental or full-replace // faq ## Common questions. About depop.com scraping, legality, and pipeline operations. [Ask us directly →](https://dataflirt.com/contact/) Is scraping Depop legal? Scraping publicly available listing, pricing, and seller data from Depop is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated data. We do not extract personal data, private messages, or purchase history. We recommend clients review Depop's ToS independently and consult legal counsel for specific use cases. Can you extract sold listing prices — not just active asking prices? Yes. This is one of the most valuable aspects of our Depop pipeline. Sold listings remain visible on Depop with a sold badge. We separately extract sold listings with their final price, original ask price, likes count at time of sale, and sold date — giving you real secondary market clearing prices, not just aspirational ask prices. Can you capture likes counts per listing? Yes. Likes count is scraped directly from listing pages. This is Depop's primary demand-proxy signal — high likes on an unsold listing indicates price resistance; high likes on a sold listing confirms demand. It's not available via any public API and is one of the most distinctive fields in our Depop dataset. What are style tags and why are they valuable? Style tags and aesthetic labels (Y2K, Vintage, Grunge, Cottagecore, Dark Academia, etc.) are Depop-native classification signals applied by sellers. They represent the fashion taxonomy that Gen-Z buyers actually use to discover items. Extracting them at scale enables style-trend analysis, aesthetic demand modelling, and brand-by-aesthetic segmentation that no other data source provides. Can you track specific brands across all Depop listings? Yes. Brand tag is a structured field per listing. We can scope a pipeline to specific brand terms — extracting all active and sold listings for a defined brand set — giving you a comprehensive secondary market price and demand dataset for those brands. What's the minimum viable engagement? Our smallest packages start at a defined brand or keyword set (typically 2,000–20,000 listings) with weekly delivery. For broader style taxonomy research or full seller-map programmes, we price based on volume and cadence. Can you track likes velocity over time? Yes. Every pipeline run captures timestamped likes counts per listing. Likes velocity — the rate of likes accumulation over time — is computable from the resulting time-series, and is available from the date your pipeline starts. Can I request a sample dataset before committing? Absolutely. We provide a sample run of up to 500 listings including active and sold records as part of the pre-engagement scoping process — so you can validate style tag coverage, likes completeness, and schema fit before signing any contract. $ dataflirt scope --new-project --source=depop.com ready ## Tell us what to extract. We do the rest. 20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a brand resale valuation dataset, a Gen-Z fashion trend monitor, or a sold price history feed — we scope, build, and operate the pipeline. hello@dataflirt.com · Bengaluru · IST · typical reply < 4h Services