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Data types

Datasets supported by the dataset viewer have a tabular format, meaning a data point is represented in a row and its features are contained in columns. Using the `/first-rows` endpoint allows you to preview the first 100 rows of a dataset and information about each feature. Within the `features` key, you’ll notice it returns a `_type` field. This value describes the data type of the column, and it is also known as a dataset’s `Features`.

There are several different data `Features` for representing different data formats such as `Audio` and `Image` for speech and image data respectively. Knowing a dataset feature gives you a better understanding of the data type you’re working with, and how you can preprocess it.

For example, the `/first-rows` endpoint for the Rotten Tomatoes dataset returns the following:

    {"dataset": "cornell-movie-review-data/rotten_tomatoes",
     "config": "default",
     "split": "train",
     "features": [{"feature_idx": 0,
       "name": "text",
       "type": {"dtype": "string",
       "id": null,
       "_type": "Value"}},
      {"feature_idx": 1,
       "name": "label",
       "type": {"num_classes": 2,
        "names": ["neg", "pos"],
        "id": null,
        "_type": "ClassLabel"}}],
      ...
     }

This dataset has two columns, `text` and `label`:

- The `text` column has a type of `Value`. The `Value` type is extremely versatile and represents scalar values such as strings, integers, dates, and even timestamp values.

- The `label` column has a type of `ClassLabel`. The `ClassLabel` type represents the number of classes in a dataset and their label names. Naturally, this means you’ll frequently see `ClassLabel` used in classification datasets.

For a complete list of available data types, take a look at the `Features` documentation.

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