Introduction

As the name suggests, time series datasets refer to a certain kind of datasets where each data point is associated with a timestamp. This is a common format for datasets that are collected over time, such as satellite data.

This section covers:

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Terminology

Here are some terms used throughout this section.

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Creating a datasets Client

Prerequisites

• You’ve installed the tilebox-datasets package
• You’ve created a Tilebox API key

With the prerequisites out of the way, you can now create a client instance to start interacting with your Tilebox Datasets.

As an alternative, you can also set the TILEBOX_API_KEY environment variable to your API key and instantiate the client without passing the token argument. Python automatically pick up the environment variable and use it to authenticate with the API.

Tilebox datasets offers a standard synchronous API by default, but also give you the option of an async client if you need it.

The synchronous client is great for data exploration in interactive environments like Jupyter notebooks. The asynchronous client is great for building production ready applications that need to scale. To find out more about the differences between the two clients, check out the Async support page.

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Exploring datasets

Now that you have a client instance, you can start exploring the datasets that are available. An easy way to do this is to list all datasets and then using the autocomplete capability of your IDE or inside your Jupyter notebook.

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Errors you might encounter

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AuthenticationError

AuthenticationError is raised when the client is unable to authenticate with the Tilebox API. This can happen when the provided API key is invalid or expired. Instantiating a client with an invalid API key does not raise an error directly, but only when you try to make a request to the API.

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Next steps