polaroids 0.1.0

Validate you Generic polars dataframes

Polars Generic

This package provides a generic extension to Polars DataFrame, allowing schema validation and custom validation logic through class-based definitions.

Features

• Generic DataFrame: Ensures type safety using Python's TypedDict.
• Schema Validation: Automatically checks that the DataFrame conforms to the expected schema.
• Custom Validation Hooks: Define additional validation methods prefixed with check_.
• Improved Typing for Rows: Provides better type safety for rows(named=True).

Installation

pip install polaroids

Usage

Defining a Schema

Schemas are defined using Python's TypedDict:

from typing import TypedDict
from polaroids import DataFrame, Field
import polars as pl

class BasicSchema(TypedDict):
    a: Annotated[pl.Int64, Field(
        sorted="ascending",
        coerce=True,
        unique=True,
        checks=[lambda d: d.ge(0)],
    )]
    b: str

df = pl.DataFrame({"a": [0, 1], "b": ["a", "b"]})
basic_df = DataFrame[BasicSchema](df)
basic_df.validate()  # Ensures schema correctness

Adding Custom Validations

Extend DataFrame and define validation methods prefixed with check_:

class BasicSchemaDataFrame(DataFrame[BasicSchema]):
    def check_a_is_positive(self) -> Self:
        assert self.select(pl.col("a").ge(0).all()).item(), "Column a contains negative values!"
        return self

# Example usage
df = pl.DataFrame({"a": [0, 1]})
basic_df = BasicSchemaDataFrame(df)
basic_df.validate()  # Passes validation

# This will raise an AssertionError
df_invalid = pl.DataFrame({"a": [-1, 1]})
BasicSchemaDataFrame(df_invalid).validate()

Get typing goodies !

Ensure row retrieval maintains proper types:

row = basic_df.rows(named=True)[0]
# row is a typedDict !