fastdataframe 0.0.5

A fast dataframe implementation with Pydantic integration

FastDataFrame

FastDataFrame is a modern Python library that bridges the world of Pydantic models and dataframe libraries, providing a standard interface for validating, transforming, and creating dataframes from Pydantic models. It currently supports:

• Polars DataFrame and LazyFrame
• Apache Iceberg tables

The goal is to make it easy to:

• Validate a dataframe against a Pydantic model schema
• Convert a dataframe to an iterable of Pydantic models
• Create a dataframe from a list of Pydantic models, with correct types

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Features

• Schema Validation: Ensure your dataframe matches your Pydantic model's schema.
• Model Conversion: Easily convert between dataframes and Pydantic models.
• Type-Safe DataFrames: Automatically infer and enforce correct column types.
• Multi-Backend: Works with Polars and Iceberg (with plans for Pandas, PyArrow, and more).

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Polars Integration

1. Define Your Pydantic Model

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    age: int

2. Create a Polars DataFrame from Models

import polars as pl
from fastdataframe.polars.model import PolarsFastDataframeModel

# Create a Polars-compatible model
PolarsUser = PolarsFastDataframeModel.from_base_model(User)

# List of Pydantic models
users = [
    User(id=1, name="Alice", age=30),
    User(id=2, name="Bob", age=25),
]

# Create a DataFrame
df = pl.DataFrame([user.model_dump() for user in users])

# Cast DataFrame to match the model schema (enforces types)
df = PolarsUser.cast_to_model_schema(df)
print(df)

3. Validate a DataFrame Against the Model

errors = PolarsUser.validate_schema(df)
if errors:
    print("Validation errors:", errors)
else:
    print("DataFrame is valid!")

4. Convert DataFrame Rows to Pydantic Models

models = [PolarsUser(**row) for row in df.to_dicts()]
print(models)

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Iceberg Integration

1. Define Your Pydantic Model

from pydantic import BaseModel

class Transaction(BaseModel):
    transaction_id: str
    amount: float
    timestamp: str

2. Generate Iceberg Schema from Model

from fastdataframe.iceberg.model import IcebergFastDataframeModel

IcebergTransaction = IcebergFastDataframeModel.from_base_model(Transaction)
iceberg_schema = IcebergTransaction.get_iceberg_schema()
print(iceberg_schema)

3. Validate Iceberg Table Schema

# Suppose you have an Iceberg table schema as a dict
table_schema = {
    "fields": [
        {"name": "transaction_id", "type": "string"},
        {"name": "amount", "type": "double"},
        {"name": "timestamp", "type": "string"},
    ]
}

errors = IcebergTransaction.validate_schema(table_schema)
if errors:
    print("Schema validation errors:", errors)
else:
    print("Iceberg schema is valid!")

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Why FastDataFrame?

• Type Safety: Enforce your data contracts at the dataframe level.
• Consistency: Use the same Pydantic models for both API and data processing.
• Extensible: Designed to support multiple dataframe backends.

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Installation

uv pip install fastdataframe
# or
pip install fastdataframe

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Roadmap

• Polars support
• Iceberg support
• Pandas support
• PyArrow support
• PyIceberg support

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License

MIT