We extract fibre metrics, global yield statistics, yarn profiles, and trade data from cottonguide.org. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Fibre Properties objects from cottonguide.org. All fields typed and schema-versioned.
"origin_country": "Mali", "crop_year": "2024/25", "staple_length_mm": 28.5, "micronaire_value": 4.2, "tensile_strength_gpt": 29.1, "uniformity_index": 81.5, "colour_grade": "Middling", "trash_content_pct": 2.4
| # | origin_country | crop_year | staple_length_mm | micronaire_value | tensile_strength_gpt | uniformity_index |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market Prices objects from cottonguide.org. All fields typed and schema-versioned.
"date": "2025-10-14", "index_name": "Cotlook A", "price_usd_lb": 0.894, "physical_price": 0.912, "futures_price": 0.88, "basis_points": 320, "origin_region": "West Africa", "destination_port": "Qingdao"
| # | date | index_name | price_usd_lb | currency | physical_price | futures_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Yield Statistics objects from cottonguide.org. All fields typed and schema-versioned.
"country": "India", "region": "Gujarat", "season": "2024", "planted_area_ha": 2540000, "harvested_area_ha": 2510000, "yield_kg_ha": 645, "production_mt": 1618950, "export_volume_mt": 320000
| # | country | region | season | planted_area_ha | harvested_area_ha | yield_kg_ha |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Yarn Specifications objects from cottonguide.org. All fields typed and schema-versioned.
"yarn_count_ne": 30, "spinning_method": "Ring Spun", "twist_multiplier": 3.8, "tenacity_cn_tex": 16.5, "unevenness_cv_pct": 14.2, "neps_count_km": 185, "hairiness_index": 4.1, "end_use_category": "Knitting"
| # | yarn_count_ne | spinning_method | twist_multiplier | tenacity_cn_tex | elongation_pct | unevenness_cv_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sustainability Metrics objects from cottonguide.org. All fields typed and schema-versioned.
"certification_type": "Better Cotton Initiative (BCI)", "country": "Brazil", "certified_farms_count": 342, "certified_area_ha": 1250000, "production_volume_mt": 2100000, "premium_usd_mt": 45.0, "standards_version": "v2.1", "issue_date": "2025-01-15"
| # | certification_type | country | certified_farms_count | certified_area_ha | production_volume_mt | premium_usd_mt |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Cottonguide.org hosts thousands of pages of technical textile data, market reports, and statistical tables. We automate the extraction of this data into structured formats, handling complex nested tables and PDF reports.
Extract Micronaire, staple length, strength, and uniformity parameters across different origins and crop years.
Capture daily and weekly index prices, physical market rates, and futures basis points.
Scrape country-level and regional statistics for planted area, harvested area, and yield per hectare.
Extract technical specifications for ring-spun and rotor-spun yarns, including tenacity and neps counts.
Track BCI, organic, and Fairtrade production volumes, certified farm counts, and price premiums.
Extract import duties, export quotas, and bilateral trade agreement details affecting cotton movement.
Extract structured tabular data from embedded ITC reports and market summary PDFs.
Link origin yield statistics to global export markets and destination port pricing.
Automate extraction runs to capture monthly crop estimates and weekly price bulletin updates.
Brief in. Clean data out.
Specify required data points: fibre properties, yield stats, or market prices. We map the target URLs.
We configure crawlers, handle table extraction logic, and implement PDF parsing for embedded reports.
Data types are normalised, units are standardised, and null-rates are checked against historical baselines.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from technical portals involves handling nested tables, inconsistent historical formats, and embedded documents. Here is our approach.
Much of the historical market data is locked in PDF reports. We deploy pdfplumber and custom heuristic scripts to identify table boundaries, extract cell values, and reconstruct the data into flat, queryable records.
Yields may be reported in bales, metric tonnes, or pounds depending on the publication year. Our pipeline includes a normalisation layer that converts all units to a standard metric baseline before delivery.
Statistical tables on the site often use complex colspans and rowspans. We map these structures algorithmically to ensure parent categories (e.g., region) are correctly attributed to child rows (e.g., specific farm data).
Archival data spanning decades often features different formatting conventions. We maintain versioned extraction schemas that apply specific parsing logic based on the document's publication date.
To prevent overloading the host servers, we enforce strict concurrency limits, implement exponential backoff, and cache static resources during the crawl process.
Spinning mills analyse fibre properties across different origins to optimise blend costs and source ideal staple lengths.
Commodity analysts ingest yield statistics and planted area data to model future supply and predict price movements.
Apparel brands track BCI and organic cotton production volumes to verify supply chain sustainability claims.
Agronomists and textile engineers use historical fibre data to study the impact of climate variations on cotton quality.
Trade organisations monitor import tariffs, export quotas, and government subsidies affecting the global textile trade.
Yarn manufacturers compare their technical specifications against global standards published for specific end-uses.
"Global textile supply chains rely on accurate fibre and yield data, but manual extraction from technical portals introduces errors and delays."
Extracting data from cottonguide.org requires handling complex statistical tables, unit conversions, and PDF reports. DataFlirt automates this entire process, delivering clean, normalised datasets so your analysts can focus on market forecasting rather than data entry.
Everything supported by our cottonguide.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy manages the crawl orchestration and HTML table parsing, while Playwright handles any dynamic visualisations or JavaScript-rendered statistical charts.
We integrate pdfplumber and Pandas directly into the extraction pipeline to convert unstructured report documents into clean, typed dataframes before export.
Pipelines run on Kubernetes clusters. Airflow triggers extraction runs based on publication schedules, ensuring data is captured as soon as new reports are released.
Data delivered to where your team already works — no new tooling required.
About cottonguide.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible statistical data, reports, and standards from cottonguide.org is generally permissible. DataFlirt extracts only public information and does not attempt to bypass authentication for confidential proprietary data. Clients should ensure their use of the data complies with ITC copyright and redistribution policies.
Yes. Our pipeline uses specialised libraries to identify and extract tabular data from PDF files, converting unstructured documents into structured JSON or CSV formats.
We implement a normalisation layer. Whether yield is reported in bales, metric tonnes, or pounds, we convert and standardise all metrics to a single unit (e.g., metric tonnes) based on your schema requirements.
Extraction frequency is tied to the publication schedule of the source material. We typically configure pipelines to run weekly for price updates and monthly for crop estimates and yield statistics.
Yes. We can perform initial backfill runs to extract archival data, historical price series, and past crop year statistics available on the platform.
We monitor pipeline health continuously. If a structural change causes extraction failures or null-rate spikes, our engineering team updates the parsing logic to restore the flow of data.
Yes. We provide sample datasets covering specific crop years or fibre properties during the scoping phase to validate schema design and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually copying tables from PDF reports. We build and maintain the pipelines to deliver clean, structured cotton market data directly to your warehouse.
