This documentation page is for backwards compatibility. For new projects, we recommend using the new semantic dataframes.

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Migrate SmartDatalakes

To migrate from SmartDatalake to the new semantic dataframes:

  1. Use the new multiple dataframe syntax with pai.chat()
  2. Update configuration to use pai.config.set()

Example migration:

# Old code
from pandasai import SmartDatalake, SmartDataframe
lake = SmartDatalake([
    SmartDataframe(customers_df),
    SmartDataframe(orders_df)
])
response = lake.chat("Query across dataframes")

# New code
import pandasai as pai
df_customers = pai.DataFrame(customers_df)
df_orders = pai.DataFrame(orders_df)
response = pai.chat("Query across dataframes", df_customers, df_orders)

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SmartDatalakes (Legacy)

The SmartDatalake class was the previous way to work with multiple dataframes in PandasAI. It allowed you to combine multiple SmartDataframes and ask questions that span across them.

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Basic Usage

from pandasai import SmartDatalake, SmartDataframe
import pandas as pd

# Create sample dataframes
customers_df = pd.DataFrame({
    'customer_id': [1, 2, 3],
    'name': ['John', 'Emma', 'Alex']
})
orders_df = pd.DataFrame({
    'order_id': [101, 102, 103],
    'customer_id': [1, 2, 1],
    'amount': [100, 200, 150]
})

# Convert to SmartDataframes
smart_customers = SmartDataframe(customers_df)
smart_orders = SmartDataframe(orders_df)

# Create SmartDatalake
lake = SmartDatalake([smart_customers, smart_orders])

# Ask questions spanning multiple dataframes
response = lake.chat("What is the total order amount for each customer?")

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Configuration

SmartDatalake inherited configuration from its SmartDataframes, but you could also set config directly:

lake = SmartDatalake(
    [smart_customers, smart_orders],
    config={
        "llm": llm,
        "verbose": True
    }
)