Dataset Viewer

Split (1)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 6 fields in line 16, saw 7

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 6 fields in line 16, saw 7

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Spanish Coffee Taste & Preferences — Business Intel Dataset

A two-part dataset built as business intelligence for coffee-related decisions in Spain (product/menu, pricing, location/regional strategy, and marketing/trends):

real/ — curated, sourced facts about the Spanish coffee market from public primary sources (AECafé, MAPA, INE, CBI, industry studies).
synthetic/ — a simulated individual-level consumer survey calibrated to the real marginals above. Every row is synthetic. No real individuals exist.

Why two parts

Real public data is trustworthy but aggregated — it tells you how much, not who. The synthetic survey bridges that gap for segmentation and hypothesis-testing, but it is simulated, not evidence. Never base a decision solely on the synthetic rows; use them to explore segments, then validate against the real facts or your own field data.

Structure

Path	Contents
`real/consumption_overview.csv`	National consumption: 4.19 kg/person/year, ~66M cups/day, market volume and value, home vs HORECA split
`real/regional_consumption.csv`	Per-CCAA daily cups, per-capita leaders (La Rioja 2.0, País Vasco 1.8, Asturias 1.7), home-bound vs out-of-home-heavy regions
`real/roast_format_share.csv`	Roast/format shares: home natural 41 / mezcla 27 / capsules 18 / soluble 14; HORECA natural 77; torrefacto contradiction flagged
`real/drink_type_shares.csv`	Drink preferences: 2019 general survey (con leche 37 / solo 27 / cortado 24) vs 2024 bar POS (81% milk drinks)
`real/prices.csv`	Bar prices (solo €1.00-1.30), specialty café prices (espresso €2.14, flat white €3.14), cup cost structure (~€0.70)
`real/prices_per_kg.csv`	Verified per-kg retail prices: commercial €6.08-15.80/kg (Hacendado, Marcilla, Bonka) vs specialty €33-90/kg (Boconó, La Guashira, Café Raizal)
`real/cup_economics.csv`	Derived per-cup economics: dose per cup, coffee cost per cup, ticket prices, margin %, kg-equivalents
`real/prices_horeca.csv`	HORECA wholesale per-kg: commercial blends €11-17/kg (Saimaza, Bonka, Kfetea, Mocaibo) vs specialty ~€25-35/kg (Raizal verified, rest quote-on-request)
`real/horeca_suppliers.csv`	Competitor panel of 14 HORECA coffee suppliers: 17 verified list prices (€11.95-31.67/kg), roast profiles, minimums, services (training, white label), and which suppliers quote on request
`real/prices_city.csv`	Café con leche prices by city, 2024
`real/price_inflation_2024.csv`	INE HICP regional coffee price inflation extremes 2024
`real/market_indicators.csv`	Decaf (~20%, largest EU market), specialty trend cities, sustainability gap (Rainforest-certified imports 2.7%), gender/age preferences
`synthetic/survey_schema.csv`	Column-by-column calibration basis: every column marked REAL (sourced) or ASSUMPTION
`derived/analisis_precios_y_descuentos.xlsx`	Excel interactivo: resumen de pricing recomendado, simulador de márgenes con fórmulas vivas (edita el coste y recalcula), panel de competidores HORECA, escalas de descuento por volumen, precios per-kg retail y economía por taza
`derived/infografia_precios_descuentos.svg`	Infografía de consulta rápida (ELI5): coste por kilo, escalera de precios de venta, qué cobran los demás, lo que cuesta por taza al cliente y las dos escalas de descuento del mercado
`derived/infografia_cuentas_25kg.svg`	Infografía ELI5: las cuentas de vender a €25/kg con descuentos — margen base €3.23/kg, impacto de cada escala de descuento (−15% = pérdida), margen por tipo de cliente, año completo y veredicto
`derived/infografia_margen_26_28.svg`	Infografía ELI5: márgenes a €26-28/kg — €4.14-5.95/kg de margen base, tabla de resistencia a descuentos (a €28, incluso con −15% ganas €2.14/kg), margen por cliente y año completo
`synthetic/survey_responses.csv`	Generated by `scripts/generate_survey.py` — run the script to produce it (deterministic, seed 42)
`synthetic/survey_sample_60rows.csv`	Hand-authored 60-row preview of the survey structure
`scripts/generate_survey.py`	Stdlib-only generator: `python scripts/generate_survey.py [N_ROWS] [SEED]`
`report.md`	Key findings and how to read the data
`AGENTS.md`	Instructions for AI agents: loading code, file-selection map, data-handling rules

Known gaps and contradictions (read before using)

Torrefacto share: CBI says 12.5% of all coffee is pure torrefacto (declining); mezcla-based proxies suggest ~27%. Range given, not a point estimate.
Per-capita consumption: 4.19 kg (AECafé, incl. soluble) vs ~3.3 kg (roasted only). Different definitions — the tables state which.
Drink-type shares: the 37/27/24 split is from a 2019 home-centric survey; the 2024 Square split is bar-POS-centric and skews modern/urban. No current unified source exists.
Regional drink preferences (e.g., cortado vs con leche by CCAA) are undocumented; only volumes and home/HORECA shares exist per region. Regional taste variation in the synthetic survey is modeled assumption.
Sweetening: real sources only say "majority sweeten" — the quantitative split in the survey is an assumption.
Sustainability: stated importance (modeled) is deliberately higher than revealed behavior (2.7% certified imports).

Primary sources

AECafé, Informe Sectorial del Café en España 2023-24 and 2024-25 — asociacioncafe.com/datos-cafe/
MAPA Panel de Consumo en los Hogares — mapa.gob.es (kg, spend, price, penetration per CCAA, monthly since 1990)
CBI, Spanish coffee market potential (2025) — cbi.eu
Mercasa, Alimentación en España: Café
Square POS data via InfoHoreca (2024); Diario de Gastronomía (2019 AECafé survey); El País (2025-09-30); EFEAgro (2023); INE HICP regional data (2024)

For AI agents

If you are an AI agent (or building one) consuming this dataset, read AGENTS.md first: it has ready-to-use loading code (pandas / datasets / hf_hub_download), a task-to-file map, and hard rules — above all, that synthetic/ rows are simulated and must never be reported as evidence.

License

Real-facts tables: facts are attributed to their sources above; use under CC-BY-4.0 with attribution. Synthetic data: CC-BY-4.0.

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