One common use case of contexts is the creation of datasets. Each dataset has it’s own context, such that it can easily be viewed in it’s own table.

The unify.Dataset class is the best way to interact with your datasets from the Python client.

my_dataset = unify.Dataset([0, 1, 2], name="my_dataset")

You can then upload this to your interface like so.

my_dataset.upload()

unify.Dataset sets the context as f"Datasets/{name}", so your dataset can be viewed at Datasets/my_dataset.

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If your dataset already exists upstream, you can download it like so.

my_dataset = unify.Dataset.from_upstream("my_dataset")

You can also add and remove content by value using standard Python operators.

my_dataset += [3, 4, 5]
my_dataset -= [0, 1, 2]
my_dataset.upload(overwrite=True)

Given that logs can be shared across multiple contexts, it’s easy to create sub-datasets withou duplication.

import random
my_dataset = unify.Dataset(
    list(range(100)), name="my_dataset"
).upload()
my_sub_dataset = unify.Dataset(
    random.sample(my_dataset, 10), name="my_sub_dataset"
).upload()

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Editing a log (row) in one dataset will be reflected in the other.

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Logs can also be removed from one dataset table, without deleting it from the other. However, if the log is deleted from both datasets, then it will be deleted entirely.

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ContextsColumn Contexts