> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Export Data API

> Export production data from Arize AX into notebooks and Phoenix for further analysis using the Python export client.

<Info>
  API reference [here](https://arize-client-python.readthedocs.io/en/latest/llm-api/exporter.html). ML Version [here](https://arize-client-python.readthedocs.io/en/latest/ml-api/exporter.html).
</Info>

Arize AX enables teams to continue analysis of their production data in notebooks.

With a few lines of Python code, users can export their data into Phoenix or a Jupyter notebook for further analysis.

There are two ways to do this:

1. The easiest way is to click the export button on the Embeddings, Datasets, or Performance tracing pages. This will produce a code snippet that you can copy into a Python environment. This code snippet will include the date range you have selected in Arize AX, in addition to the datasets you have selected.

<Frame>
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/export-data-1.avif" alt="Export button the Datasets tab" />
</Frame>

<Frame>
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/export-data-2.avif" alt="Export button on Embeddings tab" />
</Frame>

<Frame>
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/export-data-3.avif" alt="Export to Phoenix example" />
</Frame>

2. Users can also query Arize AX for data directly using the Arize Python export client. We recommend doing this once you're more comfortable with the in-platform export functionality, as you will need to manually enter in the data ranges and datasets you want to export.

```python theme={null}
os.environ['ARIZE_API_KEY'] = ARIZE_API_KEY

from datetime import datetime

from arize.exporter import ArizeExportClient
from arize.utils.types import Environments

client = ArizeExportClient()

primary_df = client.export_model_to_df(
    space_id='U3BhY2U6NzU0',
    model_id='test_home_prices_LLM',
    environment=Environments.PRODUCTION,
    start_time=datetime.fromisoformat('2023-02-11T07:00:00.000+00:00'),
    end_time=datetime.fromisoformat('2023-03-14T00:59:59.999+00:00'),
    # Optionally specify columns to improve query performance
    # columns=['context.span_id', 'attributes.llm.input']
)
```

<Info>
  Questions? Email us at [support@arize.com](mailto:support@arize.com) or [Slack us](https://arize-ai.slack.com/) in the #arize-support channel
</Info>