Introduction

Time series datasets refer to datasets where each data point is linked to a timestamp. This format is common for data collected over time, such as satellite data.

This section covers:

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Terminology

Get familiar with some key terms when working with time series datasets.

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Creating a datasets client

Prerequisites

• You have installed the tilebox-datasets package.
• You have created a Tilebox API key.

After meeting these prerequisites, you can create a client instance to interact with Tilebox Datasets.

from tilebox.datasets import Client

client = Client(token="YOUR_TILEBOX_API_KEY")

You can also set the TILEBOX_API_KEY environment variable to your API key. You can then instantiate the client without passing the token argument. Python will automatically use this environment variable for authentication.

from tilebox.datasets import Client

# requires a TILEBOX_API_KEY environment variable
client = Client()

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Exploring datasets

After creating a client instance, you can start exploring available datasets. A straightforward way to do this in an interactive environment is to list all datasets and use the autocomplete feature in your Jupyter notebook.

datasets = client.datasets()
datasets. # trigger autocomplete here to view available datasets

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Errors you might encounter

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AuthenticationError

AuthenticationError occurs when the client fails to authenticate with the Tilebox API. This may happen if the provided API key is invalid or expired. A client instantiated with an invalid API key won’t raise an error immediately, but an error will occur when making a request to the API.

client = Client(token="invalid-key") # runs without error
datasets = client.datasets() # raises AuthenticationError

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Next steps