Pandera DataFrame Validation

Validate pandas DataFrames with pandera schemas. Defines column checks, dtype and range constraints, class-based schema models, and decorator-based validation so bad data fails fast in your pipelines.

This skill applies pandera for statistical DataFrame validation. It writes schema definitions with column-level checks, nullability and dtype rules, value ranges and custom checks, class-based DataFrameModel schemas, and decorator-based validation on function inputs and outputs. Includes error-collection patterns so you get every failing row at once instead of failing on the first.

When to use

Use when validating pandas DataFrames, writing pandera schemas or DataFrameModels, adding column checks and dtype constraints, or decorating pipeline functions with input/output validation.

Examples

Schema for a DataFrame

Define column checks

Write a pandera schema for my orders DataFrame: order_id unique and not null, amount a positive float, and status in a fixed set of values

Class-based model

Use a DataFrameModel

Convert my pandera column dict into a class-based DataFrameModel and validate it as a function type hint

Collect all errors

Report every bad row

Make my pandera validation collect all failures with lazy=True and return a summary of every failing column and row