| name | pandasai |
| description | Conversational data analysis using natural language queries on pandas DataFrames. Use when you want to ask plain-English questions about data, generate charts, explain transformations, or build exploratory analysis interfaces — all powered by an LLM backend. Supports OpenAI, Anthropic, Google Gemini, Azure OpenAI, and local models. Handles single DataFrames (SmartDataframe) and multi-table joins (SmartDatalake).
|
| version | 1.0.0 |
| author | workspace-hub |
| category | ai-prompting |
| type | skill |
| trigger | manual |
| auto_execute | false |
| capabilities | ["natural_language_queries","dataframe_conversations","chart_generation","code_explanation","multi_dataframe_analysis","custom_prompts","llm_backend_flexibility","data_privacy_modes"] |
| tools | ["Read","Write","Bash","Grep"] |
| tags | ["pandasai","llm","dataframe","natural-language","data-analysis","visualization","conversational-ai","pandas"] |
| platforms | ["python"] |
| related_skills | ["langchain","pandas-data-processing","plotly","streamlit"] |
| scripts_exempt | true |
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