preco_diario v1.0¶

Daily spot prices of Brazilian agricultural commodities.

Sources¶

Priority	Source	Description
1	CEPEA/ESALQ	Via Notícias Agrícolas
2	Local cache	DuckDB

Products¶

soja, milho, boi, cafe, cafe_robusta, trigo, algodao

Schema¶

Column	Type	Nullable	Unit	Description
`data`	datetime64	❌	-	Indicator date
`produto`	str	❌	-	Product name
`praca`	str	✅	-	Reference market
`valor`	float64	❌	BRL	Price in reais
`unidade`	str	❌	-	E.g. "BRL/sc60kg"
`fonte`	str	❌	-	Data origin

Precision note: valor uses float64 (not Decimal) for compatibility with pandas/polars and pipeline performance. IEEE 754 precision is sufficient for agricultural prices (max ~R$ 999,999.99). For accounting use that requires exact precision, convert with df["valor"].apply(Decimal) after the fetch.

Guarantees¶

data is always a business day
valor is always positive
Sorted by data descending

Example¶

from agrobr import datasets

# Async
df = await datasets.preco_diario("soja")
df, meta = await datasets.preco_diario("soja", return_meta=True)

# Sync
from agrobr.sync import datasets
df = datasets.preco_diario("soja")

Deterministic Mode¶

from agrobr import datasets

async with datasets.deterministic("2025-12-31"):
    df = await datasets.preco_diario("soja")
    # Filters data <= 2025-12-31
    # Uses local cache only

JSON Schema¶

Available at agrobr/schemas/preco_diario.json.

from agrobr.contracts import get_contract
contract = get_contract("preco_diario")
print(contract.primary_key)  # ['data', 'produto']
print(contract.to_json())

MetaInfo¶

When return_meta=True, returns a (DataFrame, MetaInfo) tuple:

df, meta = await datasets.preco_diario("soja", return_meta=True)

print(meta.source)            # "datasets.preco_diario/cepea"
print(meta.dataset)           # "preco_diario"
print(meta.contract_version)  # "1.0"
print(meta.records_count)     # 365
print(meta.from_cache)        # False
print(meta.snapshot)          # None (or "2025-12-31" if deterministic)