Loading and Inspecting Datasets

Pull a dataset by name

import zeroeval as ze

ze.init()

dataset = ze.Dataset.pull("capital-cities")
print(dataset.name)
print(len(dataset))

Optionally load a specific version number:

dataset_v2 = ze.Dataset.pull("capital-cities", version_number=2)

Rows are yielded as DotDict objects, so both key and dot access are possible.

for row in dataset:
    print(row.question, row.answer)  # dot access

first = dataset[0]
top_100 = dataset[:100]

print(type(first))    # DotDict
print(type(top_100))  # Dataset

Sliced datasets preserve backend metadata (dataset id/version/subset) when available, so they can still be evaluated and pushed in normal workflows.

print(dataset.columns)  # union of all row keys (excluding internal row_id)
print(dataset.data)     # row payloads without wrapper metadata

SDK not initialized

Dataset.pull(...) requires a valid ZeroEval initialization.

ze.init(api_key="sk_ze_...")

Dataset not found

Confirm dataset name and project context (API key/org mapping). Pull uses the project resolved from your API credentials.