---
title: Expand your data | Adaption
description: Augment, translate, or localize rows to increase dataset size after import or adaptation.
---

After you [import a dataset](/adaptive-data/create-a-dataset/index.md) and optionally [run Adaptive Data](/api/python/resources/datasets/methods/run/index.md), you can increase row count without re-running the full adaptation pipeline. Each method below returns a **new dataset** that includes your source rows plus the added ones.

The SDK examples assume you have [configured the Python SDK](/introduction/getting-started/index.md) and set `ADAPTION_DATASET_ID` to a dataset that is ready to expand:

```
import os


from adaption import Adaption


client = Adaption()
dataset_id = os.environ["ADAPTION_DATASET_ID"]
```

## Data augmentation

[`datasets.augment`](/api/python/resources/datasets/methods/augment/index.md) retrieves additional rows from the curated pool and returns them as a new dataset. Use it when you want more examples in the same topic mix or broader coverage across topics:

- **Domain rows** match topics in your dataset.
- **General rows** come from other topics to broaden diversity.

```
estimate = client.datasets.augment(
    dataset_id,
    domain_rows=3_000,
    general_rows=1_000,
    estimate=True,
)
print(f"Estimated credits: {estimate.estimated_credits_consumed}")


response = client.datasets.augment(
    dataset_id,
    domain_rows=3_000,
    general_rows=1_000,
)
print(response.dataset_id, response.status)
```

The response carries the new dataset’s id and its current status; both are `None` for an estimate. Wait for the new dataset before you download or train on it:

```
from adaption import DatasetTimeout


try:
    final = client.datasets.wait_for_completion(response.dataset_id)
except DatasetTimeout as exc:
    print(exc.resource_id, exc.last_status)
    raise


print(final.status, final.row_count)
```

`wait_for_completion` backs off from 2 to 30 seconds and has a one-hour default timeout. A timeout stops waiting, not the expansion job. Call it again with the same dataset ID to resume tracking. The same wait applies after [`translate`](#translate-rows) and [`localize`](#localize-rows).

Omit either row parameter to skip that type of addition. Augmentation is billed on the rows retrieved. See the [`augment` reference](/api/python/resources/datasets/methods/augment/index.md) for current limits and request fields.

`datasets.augment` works on text datasets whose topic mix can be determined. It does not support image datasets. This is the same curated-pool operation as `augmentation_domain_rows` and `augmentation_general_rows` on [`autoscientist.create`](/autoscientist/data-augmentation/index.md). The AutoScientist path applies those rows at training time instead of writing a new dataset.

## Translate rows

[`datasets.translate`](/api/python/resources/datasets/methods/translate/index.md) converts a sample of existing rows into other languages. Adaptive Data supports up to 242 languages. Provide [ISO 639-1 language codes](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes). `sample_rate` is the fraction of source rows expanded for each target language:

```
estimate = client.datasets.translate(
    dataset_id,
    sample_rate=0.25,
    languages=["es", "fr"],
    estimate=True,
)
print(f"Estimated credits: {estimate.estimated_credits_consumed}")


response = client.datasets.translate(
    dataset_id,
    sample_rate=0.25,
    languages=["es", "fr"],
)
print(response.dataset_id, response.status)
```

With two target languages and a `0.25` sample rate, the output contains approximately `1 + (0.25 × 2) = 1.5` times as many rows as the input.

## Localize rows

[`datasets.localize`](/api/python/resources/datasets/methods/localize/index.md) adapts rows for both a language and a country, including locale-specific wording rather than direct translation alone. Provide country/language pairs using [ISO 3166-1 alpha-2 country codes](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements) and [ISO 639-1 language codes](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes):

```
response = client.datasets.localize(
    dataset_id,
    sample_rate=0.25,
    pairs=[
        {"country": "CA", "language": "fr"},
        {"country": "MX", "language": "es"},
    ],
)
print(response.dataset_id, response.status)
```

Each pair creates a country- and language-specific variant rather than only translating the text.

Translate and localize are billed on output rows. Set `estimate=True` on any of these methods to price a request before it runs. Pass `idempotency_key` on any of them to make a retried request return the dataset the first request created instead of starting and charging for a second run. See the [`translate`](/api/python/resources/datasets/methods/translate/index.md) and [`localize`](/api/python/resources/datasets/methods/localize/index.md) references for the current schema and supported values.

For recipes, preference pairs, row limits, and brand controls on an adaptation run, see [Configure Adaptive Data](/adaptive-data/configure-adaptive-data/index.md).

The methods on this page each write a new dataset and leave the source unchanged. `datasets.run` also accepts a `language_expansion` setting; that path stores the setting on the dataset and expands rows inside its own adaptation run, without creating a new dataset. See the [`run` reference](/api/python/resources/datasets/methods/run/index.md) for that shape.