PandasAI Skill
Chat with your data using natural language. Ask questions about DataFrames and get
insights, visualizations, and explanations powered by LLMs.
When to Use
USE when:
- Exploring an unfamiliar dataset with open-ended natural language questions
- Generating quick visualizations from descriptive prompts
- Explaining complex data transformations to stakeholders
- Building conversational data exploration interfaces (Streamlit, Jupyter, FastAPI)
- Rapid prototyping of data analysis workflows
DON'T USE when:
- Production pipelines requiring deterministic, version-controlled outputs
- Processing highly sensitive PII without anonymization (use privacy mode)
- Performance-critical paths with very large DataFrames (>100K rows)
- Simple queries that direct pandas operations would handle faster
Install
uv add pandasai
uv add pandasai openai
uv add pandasai matplotlib seaborn plotly
export OPENAI_API_KEY="sk-..."
Quick Start
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
df = pd.read_csv("data.csv")
smart_df = SmartDataframe(df, config={"llm": OpenAI(model="gpt-4", temperature=0)})
result = smart_df.chat("What is the total revenue by region?")
smart_df.chat("Plot a bar chart of monthly sales")
print(smart_df.last_code_generated)
Core Capabilities
| Capability | API | Notes |
|---|
| Natural language query | smart_df.chat(question) | Returns value, DataFrame, or chart |
| Multi-table queries | SmartDatalake([df1, df2]).chat(q) | Assign df.name before passing |
| Inspect code | smart_df.last_code_generated | Verify correctness |
| Enable caching | config={"enable_cache": True} | Avoids repeat LLM calls |
| Privacy mode | config={"enforce_privacy": True} | Anonymize sensitive columns first |
| Chart saving | config={"save_charts": True, "save_charts_path": "./charts"} | Auto-saves matplotlib figures |
Supported LLM Backends
| Provider | Import class | Env var |
|---|
| OpenAI | from pandasai.llm import OpenAI | OPENAI_API_KEY |
| Anthropic | from pandasai.llm import Anthropic | ANTHROPIC_API_KEY |
| Google Gemini | from pandasai.llm import GoogleGemini | GOOGLE_API_KEY |
| Azure OpenAI | from pandasai.llm import AzureOpenAI | AZURE_OPENAI_API_KEY |
| Local (Ollama) | from pandasai.llm import LocalLLM | — |
Switch backends by passing a different llm= object to config. For fallback logic,
see MultiBackendAnalyzer in references/api-reference.md.
Privacy Pattern (Quick)
import hashlib
safe_df = df.copy()
for col in ["name", "email", "ssn"]:
safe_df[col] = safe_df[col].apply(lambda x: hashlib.md5(str(x).encode()).hexdigest()[:8])
smart_df = SmartDataframe(safe_df, config={"llm": llm, "enforce_privacy": True, "enable_cache": False})
See references/api-reference.md for full PrivacyAwareAnalyzer with audit logging.
Key Gotchas
- Determinism: Set
temperature=0 for reproducible results.
- Large DataFrames: Sample to ≤10K rows before wrapping to avoid context overflow.
- Cache invalidation: Cache keys are per-question string; data changes don't auto-invalidate.
- Chart display: In headless environments set matplotlib backend to
Agg before import.
- Multi-table: Assign
df.name = "table_name" before passing list to SmartDatalake.
References
- Full code examples (all 6 capabilities):
references/api-reference.md
- Integration patterns (Streamlit, FastAPI, Jupyter):
references/api-reference.md
- Best practices, caching, cost management:
references/api-reference.md
- Troubleshooting table:
references/api-reference.md
- Upstream docs: https://docs.pandas-ai.com/
- GitHub: https://github.com/gventuri/pandas-ai
Version History
- 1.0.0 (2026-01-17): Initial release — NL queries, chart generation, multi-backend, privacy modes
