> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Exporting Annotated Spans

> Span annotations can be an extremely valuable basis for improving your application. The Phoenix client provides useful ways to pull down spans and their associated annotations.

This information can be used to:

* build new LLM judges

* form the basis for new datasets

* help identify ideas for improving your application

### Pulling Spans

```python theme={null}
from phoenix.client import Client

client = Client()

spans = client.spans.get_spans_dataframe(
    project_identifier="default",  # you can also pass a project id
)
```

If you only want the spans that contain a specific annotation, you can pass in a query that filters on annotation names, scores, or labels.

```python theme={null}
from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness']")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)
```

The queries can also filter by annotation scores and labels.

```python theme={null}
from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness'].score == 1")
# query = SpanQuery().where("annotations['correctness'].label == 'correct'")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)
```

This spans dataframe can be used to pull associated annotations.

```python theme={null}
annotations = client.spans.get_span_annotations_dataframe(
    spans_dataframe=spans,
    project_identifier="default",
)
```

Instead of an input dataframe, you can also pass in a list of ids:

```python theme={null}
annotations = client.spans.get_span_annotations_dataframe(
    span_ids=list(spans.index),
    project_identifier="default",
)
```

The annotations and spans dataframes can be easily joined to produce a one-row-per-annotation dataframe that can be used to analyze the annotations!

```python theme={null}
annotations.join(spans, how="left")
```