# Know your dataset

There are two types of dataset objects, a regular [`Dataset`] and then an ✨ [`IterableDataset`] ✨. A [`Dataset`] provides fast random access to the rows, and memory-mapping so that loading even large datasets only uses a relatively small amount of device memory. But for really, really big datasets that won't even fit on disk or in memory, an [`IterableDataset`] allows you to access and use the dataset without waiting for it to download completely!

This tutorial will show you how to load and access a [`Dataset`] and an [`IterableDataset`].

## Dataset

When you load a dataset split, you'll get a [`Dataset`] object. You can do many things with a [`Dataset`] object, which is why it's important to learn how to manipulate and interact with the data stored inside. 
 
This tutorial uses the [rotten_tomatoes](https://huggingface.co/datasets/rotten_tomatoes) dataset, but feel free to load any dataset you'd like and follow along!

```py
>>> from datasets import load_dataset

>>> dataset = load_dataset("rotten_tomatoes", split="train")
```

### Indexing

A [`Dataset`] contains columns of data, and each column can be a different type of data. The *index*, or axis label, is used to access examples from the dataset. For example, indexing by the row returns a dictionary of an example from the dataset:

```py
# Get the first row in the dataset
>>> dataset[0]
{'label': 1,
 'text': 'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'}
```

Use the `-` operator to start from the end of the dataset:

```py
# Get the last row in the dataset
>>> dataset[-1]
{'label': 0,
 'text': 'things really get weird , though not particularly scary : the movie is all portent and no content .'}
```

Indexing by the column name returns a list of all the values in the column:

```py
>>> dataset["text"]
['the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .',
 'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .',
 'effective but too-tepid biopic',
 ...,
 'things really get weird , though not particularly scary : the movie is all portent and no content .']
```

You can combine row and column name indexing to return a specific value at a position:

```py
>>> dataset[0]["text"]
'the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .'
```

But it is important to remember that indexing order matters, especially when working with large audio and image datasets. Indexing by the column name returns all the values in the column first, then loads the value at that position. For large datasets, it may be slower to index by the column name first.

```py
>>> import time

>>> start_time = time.time()
>>> text = dataset[0]["text"]
>>> end_time = time.time()
>>> print(f"Elapsed time: {end_time - start_time:.4f} seconds")
Elapsed time: 0.0031 seconds

>>> start_time = time.time()
>>> text = dataset["text"][0]
>>> end_time = time.time()
>>> print(f"Elapsed time: {end_time - start_time:.4f} seconds")
Elapsed time: 0.0094 seconds
```

### Slicing

Slicing returns a slice - or subset - of the dataset, which is useful for viewing several rows at once. To slice a dataset, use the `:` operator to specify a range of positions. 

```py
# Get the first three rows
>>> dataset[:3]
{'label': [1, 1, 1],
 'text': ['the rock is destined to be the 21st century\'s new " conan " and that he\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .',
  'the gorgeously elaborate continuation of " the lord of the rings " trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson\'s expanded vision of j . r . r . tolkien\'s middle-earth .',
  'effective but too-tepid biopic']}

# Get rows between three and six
>>> dataset[3:6]
{'label': [1, 1, 1],
 'text': ['if you sometimes like to go to the movies to have fun , wasabi is a good place to start .',
  "emerges as something rare , an issue movie that's so honest and keenly observed that it doesn't feel like one .",
  'the film provides some great insight into the neurotic mindset of all comics -- even those who have reached the absolute top of the game .']}
```

## IterableDataset

An [`IterableDataset`] is loaded when you set the `streaming` parameter to `True` in [`~datasets.load_dataset`]:

```py
>>> from datasets import load_dataset

>>> iterable_dataset = load_dataset("food101", split="train", streaming=True)
>>> for example in iterable_dataset:
...     print(example)
...     break
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F0681F5C520>, 'label': 6}
```

You can also create an [`IterableDataset`] from an *existing* [`Dataset`], but it is faster than streaming mode because the dataset is streamed from local files:

```py
>>> from datasets import load_dataset

>>> dataset = load_dataset("rotten_tomatoes", split="train")
>>> iterable_dataset = dataset.to_iterable_dataset()
```

An [`IterableDataset`] progressively iterates over a dataset one example at a time, so you don't have to wait for the whole dataset to download before you can use it. As you can imagine, this is quite useful for large datasets you want to use immediately!

However, this means an [`IterableDataset`]'s behavior is different from a regular [`Dataset`]. You don't get random access to examples in an [`IterableDataset`]. Instead, you should iterate over its elements, for example, by calling `next(iter())` or with a `for` loop to return the next item from the [`IterableDataset`]:

```py
>>> next(iter(iterable_dataset))
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F0681F59B50>,
 'label': 6}

>>> for example in iterable_dataset:
...     print(example)
...     break
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F7479DE82B0>, 'label': 6}
```

You can return a subset of the dataset with a specific number of examples in it with [`IterableDataset.take`]:

```py
# Get first three examples
>>> list(iterable_dataset.take(3))
[{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F7479DEE9D0>,
  'label': 6},
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F7479DE8190>,
  'label': 6},
 {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x383 at 0x7F7479DE8310>,
  'label': 6}]
```

But unlike [slicing](access/#slicing), [`IterableDataset.take`] creates a new [`IterableDataset`]. 

## Next steps

Interested in learning more about the differences between these two types of datasets? Learn more about them in the [Differences between `Dataset` and `IterableDataset`](about_mapstyle_vs_iterable) conceptual guide.

To get more hands-on with these datasets types, check out the [Process](process) guide to learn how to preprocess a [`Dataset`] or the [Stream](stream) guide to learn how to preprocess an [`IterableDataset`].
