Add an entry to an arrow dataset

Is it possible to add an entry to a dataset object?

**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**

For example, say we have the following code:

``` python
from datasets import load_dataset

# Load a dataset and print the first examples in the training set
squad_dataset = load_dataset('squad')
print(squad_dataset['train'][0])
```

Is it possible to add an entry to `squad_dataset`? Something like the following?

``` python
squad_dataset.append({'text': "This is a new sentence"})
```

The motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.

If the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?



Hi @ameet-1997,
I think what you are looking for is the `concatenate_datasets` function: https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#concatenate-several-datasets

For your use case, I would use the [`map` method](https://huggingface.co/docs/datasets/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.

Let me know If this helps!

That's a great idea! Thank you so much!

When I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?

Code:
``` python
combined_dataset = concatenate_datasets([datasets, modified_dataset])
```

Error:
```
AttributeError: 'DatasetDict' object has no attribute 'features'
```

Output:
```
(Pdb) datasets
DatasetDict({
    train: Dataset({
        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
        num_rows: 493
    })
})
(Pdb) modified_dataset
DatasetDict({
    train: Dataset({
        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],
        num_rows: 493
    })
})
```

The error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?

You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`

Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.

> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.

My opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.
For instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split="train"` is given for example it's always returning a Dataset.

Thanks @thomwolf. Your solution worked!