# Setup: Python
Packages required for Phoenix evals and experiments.
## Installation
```bash
# Core Phoenix package (includes client, evals, otel)
pip install arize-phoenix
# Or install individual packages
pip install arize-phoenix-client # Phoenix client only
pip install arize-phoenix-evals # Evaluation utilities
pip install arize-phoenix-otel # OpenTelemetry integration
```
## LLM Providers
For LLM-as-judge evaluators, install your provider's SDK:
```bash
pip install openai # OpenAI
pip install anthropic # Anthropic
pip install google-generativeai # Google
```
## Validation (Optional)
```bash
pip install scikit-learn # For TPR/TNR metrics
```
## Quick Verify
```python
from phoenix.client import Client
from phoenix.evals import LLM, ClassificationEvaluator
from phoenix.otel import register
# All imports should work
print("Phoenix Python setup complete")
```
## Key Imports (Evals 2.0)
```python
from phoenix.client import Client
from phoenix.evals import (
ClassificationEvaluator, # LLM classification evaluator (preferred)
LLM, # Provider-agnostic LLM wrapper
async_evaluate_dataframe, # Batch evaluate a DataFrame (preferred, async)
evaluate_dataframe, # Batch evaluate a DataFrame (sync)
create_evaluator, # Decorator for code-based evaluators
create_classifier, # Factory for LLM classification evaluators
bind_evaluator, # Map column names to evaluator params
Score, # Score dataclass
)
from phoenix.evals.utils import to_annotation_dataframe # Format results for Phoenix annotations
```
**Prefer**: `ClassificationEvaluator` over `create_classifier` (more parameters/customization).
**Prefer**: `async_evaluate_dataframe` over `evaluate_dataframe` (better throughput for LLM evals).
**Do NOT use** legacy 1.0 imports: `OpenAIModel`, `AnthropicModel`, `run_evals`, `llm_classify`.
