Datasets

Updated 1 week ago by Alex Cota

These are some common use cases people have when working with Datasets in the Python API.

For the definition of a Dataset, see Overview & data types.

Before you start

Make sure the client is initialized.

from labelbox import Client
client = Client()

Create a Dataset

To learn how to create a Dataset, see Creating your first project.

Fetch a Dataset

Since a Dataset is a top-level object, you can get a specific Dataset by passing the unique ID of the Dataset to the client. The get_dataset method will only take a unique ID as an argument.

dataset = client.get_dataset("<dataset_id>")
print(dataset)

Fetch multiple Datasets

Below are three examples for fetching multiple datasets using the get_datasets method.

a. The code sample below demonstrates how to fetch all Datasets in your account and print the collection of Datasets by unique ID.

for dataset in client.get_datasets():
print(dataset.uid)

b. Pass a where parameter with a comparison operator. Use the get_datasets method and any of the standard comparison operators (==, !=, >, >=, <, <=) to target a Dataset. Because the get_datasets method can theoretically return any number of results, it will give you a PaginatedCollection object over which you can iterate to get your specified Dataset.

from labelbox import Dataset
datasets_x = client.get_datasets(where=Dataset.name == "X")for x in datasets_x:
print(x)

c. You can also find Datasets by using the get_datasets method to combine comparisons using logical expressions. Currently the where clause supports the logical AND operator.

from labelbox import Project
datasets = client.get_datasets(where=(Project.name == "X") & (Project.description == "Y"))
for x in datasets_x:
print(x)

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