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                          # core
uv add pandasai openai                   # + OpenAI backend
uv add pandasai matplotlib seaborn plotly # + visualization

export OPENAI_API_KEY="sk-..."
# or ANTHROPIC_API_KEY / GOOGLE_API_KEY for other backends

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")   # saves chart to ./charts/
print(smart_df.last_code_generated)                  # inspect generated code

Core Capabilities

Supported LLM Backends

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