# Experiments: Datasets in Python
Creating and managing evaluation datasets.
## Creating Datasets
```python
from phoenix.client import Client
client = Client()
# From examples
dataset = client.datasets.create_dataset(
name="qa-test-v1",
examples=[
{
"input": {"question": "What is 2+2?"},
"output": {"answer": "4"},
"metadata": {"category": "math"},
},
],
)
# From DataFrame
dataset = client.datasets.create_dataset(
dataframe=df,
name="qa-test-v1",
input_keys=["question"],
output_keys=["answer"],
metadata_keys=["category"],
)
```
## From Production Traces
```python
spans_df = client.spans.get_spans_dataframe(project_name="my-app")
dataset = client.datasets.create_dataset(
dataframe=spans_df[["input.value", "output.value"]],
name="production-sample-v1",
input_keys=["input.value"],
output_keys=["output.value"],
)
```
## Retrieving Datasets
```python
dataset = client.datasets.get_dataset(name="qa-test-v1")
df = dataset.to_dataframe()
```
## Key Parameters
| Parameter | Description |
| --------- | ----------- |
| `input_keys` | Columns for task input |
| `output_keys` | Columns for expected output |
| `metadata_keys` | Additional context |
## Best Practices
- **Versioning**: Create new datasets (e.g., `qa-test-v2`), don't modify
- **Metadata**: Track source, category, difficulty
- **Balance**: Ensure diverse coverage across categories
