Read

Reading a TACO dataset

The same logical reader opens TACO folders, ZIPs, partition lists, TACOCATs, and versioned roots from Python, R, or Julia.

01Open and read

One logical reader, three languages

Every binding resolves the same dataset. Python keeps the high-level read small and uses SQL for partial access.

import taco

dataset = taco.open_dataset("https://data.source.coop/major-tom/core-dem/")
samples = dataset.read(files="dem.rumi")
first_100 = dataset.sql("SELECT * FROM data WHERE sample_id < 100")

library(taco)

dataset <- open_dataset("https://data.source.coop/major-tom/core-dem/")
samples <- read(dataset, idx = c(0, 100), files = "dem.rumi")

using Taco

dataset = Taco.open_dataset("https://data.source.coop/major-tom/core-dem/")
samples = Taco.read(dataset; idx=(0, 100), files=["dem.rumi"])

02Python API

Read all samples or query a subset

Operation	Meaning
`dataset.read()`	Every sample, ordered by its sample key.
`dataset.read(files=...)`	The complete sample table with only the selected structural file columns.
`dataset.sql(query)`	A partial query over `data`, `files`, or a named metadata level.

03R and Julia

Native reader controls remain available

R and Julia currently expose layout, idx, level, files, and location directly. Their examples above use the same core-generated views while Python uses SQL for partial access.