pandas-ai

# Install PandasAI
pip install pandasai

# With OpenAI
pip install pandasai[openai]

# With local models via Ollama
pip install pandasai[langchain]

# basic.py: Ask questions about a DataFrame in natural language
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI

llm = OpenAI(api_token="your-openai-api-key")

df = pd.DataFrame({
    "country": ["USA", "UK", "France", "Germany", "Japan"],
    "population": [331_000_000, 67_000_000, 67_000_000, 83_000_000, 125_000_000],
    "gdp_billion": [25_460, 3_070, 2_780, 4_070, 4_230],
})

sdf = SmartDataframe(df, config={"llm": llm})

# Ask questions in natural language
answer = sdf.chat("Which country has the highest GDP?")
print(answer)  # USA

answer = sdf.chat("What is the average population?")
print(answer)  # 134,600,000

answer = sdf.chat("List countries with GDP above 4000 billion")
print(answer)

# multi-df.py: Query across multiple related DataFrames
from pandasai import SmartDatalake

employees = pd.DataFrame({
    "id": [1, 2, 3, 4, 5],
    "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
    "department_id": [1, 2, 1, 3, 2],
    "salary": [85000, 72000, 90000, 68000, 95000],
})

departments = pd.DataFrame({
    "id": [1, 2, 3],
    "name": ["Engineering", "Marketing", "Sales"],
    "budget": [500000, 200000, 300000],
})

lake = SmartDatalake([employees, departments], config={"llm": llm})

result = lake.chat("What is the average salary per department?")
print(result)

result = lake.chat("Which department is over budget based on total salaries?")
print(result)

# charts.py: Create visualizations from natural language
sdf = SmartDataframe(df, config={
    "llm": llm,
    "save_charts": True,
    "save_charts_path": "./charts",
})

# Generate charts by asking
sdf.chat("Create a bar chart of GDP by country")
sdf.chat("Plot a pie chart of population distribution")
sdf.chat("Show a scatter plot of GDP vs population")
# Charts saved as PNG in ./charts/

# cleaning.py: Use natural language for data cleaning tasks
dirty_df = pd.DataFrame({
    "name": ["Alice", "bob", "CHARLIE", None, "Eve"],
    "email": ["alice@co.com", "invalid", "charlie@co.com", "diana@co.com", ""],
    "age": [30, -5, 45, 200, 28],
    "salary": [85000, 72000, None, 68000, 95000],
})

sdf = SmartDataframe(dirty_df, config={"llm": llm})

# Clean with natural language
cleaned = sdf.chat("Remove rows where age is negative or above 150")
cleaned = sdf.chat("Fill missing salaries with the median salary")
cleaned = sdf.chat("Standardize names to title case")
cleaned = sdf.chat("Remove rows with invalid email addresses")

# config.py: Advanced PandasAI configuration
from pandasai import SmartDataframe

sdf = SmartDataframe(df, config={
    "llm": llm,
    "conversational": True,         # Natural language responses
    "verbose": True,                 # Show generated code
    "enable_cache": True,            # Cache repeated queries
    "max_retries": 3,                # Retry on LLM errors
    "custom_whitelisted_dependencies": ["scipy", "sklearn"],
    "save_logs": True,
})

# View the generated Python code
sdf.chat("What is the correlation between GDP and population?")
print(sdf.last_code_generated)

# local-llm.py: Use Ollama or other local models instead of OpenAI
from pandasai.llm.local_llm import LocalLLM

# With Ollama running locally
llm = LocalLLM(api_base="http://localhost:11434/v1", model="llama3")

sdf = SmartDataframe(df, config={"llm": llm})
answer = sdf.chat("Summarize this dataset")
print(answer)

# pipeline.py: Use PandasAI in an automated analysis pipeline
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
import pandas as pd
import json

def analyze_dataset(csv_path: str, questions: list[str]) -> dict:
    """Run a set of natural language questions against a CSV dataset."""
    llm = OpenAI(api_token="your-key")
    df = pd.read_csv(csv_path)
    sdf = SmartDataframe(df, config={"llm": llm, "conversational": True})

    results = {}
    for question in questions:
        try:
            answer = sdf.chat(question)
            results[question] = str(answer)
        except Exception as e:
            results[question] = f"Error: {e}"

    return results

# Usage
report = analyze_dataset("sales.csv", [
    "What was the total revenue last month?",
    "Which product category had the most sales?",
    "What is the month-over-month growth rate?",
])
print(json.dumps(report, indent=2))