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> Fetch the complete documentation index at: https://arize.com/llms.txt
> Ask it with GET https://arize.com/ask?q=<keywords or question>. Short keywords rank best.
> Use these 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
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
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
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
)
```

To filter spans by **trace-level** annotations (annotations attached to the
trace rather than an individual span), use `trace_annotations` in place of
`annotations`. Spans are returned when the trace they belong to carries a
matching annotation.

```python
from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("trace_annotations['quality'].score >= 0.5")
# query = SpanQuery().where("trace_annotations['quality'].label == 'good'")

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
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
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
annotations.join(spans, how="left")
```