pdschema 0.1.3

A Python library for validating pandas DataFrames using schemas

pdschema

A Python library for validating pandas DataFrames using schemas, with support for type checking, custom validators, and function input/output validation.

Features

• Define schemas for pandas DataFrames with type checking and validation
• Support for custom validators (e.g., IsPositive, IsNonEmptyString, Range, etc.)
• Function decorator for validating input and output DataFrames
• PyArrow type integration for efficient type checking
• Schema inference from existing DataFrames
• Nullable column support
• Comprehensive type mapping between Python, pandas, and PyArrow types

Installation

Using pip

pip install pdschema

Using Poetry

poetry add pdschema

Quick Start

import pandas as pd

from pdschema import Column, IsNonEmptyString, IsPositive, Range, Schema

# Create a DataFrame
df = pd.DataFrame(
    {
        "idx": [1, 2, 3],
        "name": ["Alice", "Bob", "Charlie"],
        "age": [25, 30, 35],
        "score": [85.5, 92.0, 78.5],
    }
)

# Define a schema using programmatic syntax
schema = Schema(
    [
        Column("idx", int, nullable=False),
        Column("name", str, nullable=False, validators=[IsNonEmptyString()]),
        Column("age", int, validators=[IsPositive()]),
        Column("score", float, validators=[Range(0, 100)]),
    ]
)

# Validate the DataFrame: raises ValueError if validation fails
schema.validate(df)

# Declarative Schema Definition
class MySchema(Schema):
    idx = Column(dtype=int, nullable=False)
    name = Column(dtype=str, nullable=False, validators=[IsNonEmptyString])
    age = Column(dtype=int, nullable=False, validators=[IsPositive])
    score = Column(dtype=float, nullable=False, validators=[Range(0, 100)])

MySchema().validate(df)

Function Validation

Use the @pdfunction decorator to validate function inputs and outputs:

from pdschema import pdfunction

@pdfunction(
    arguments={
        "df": Schema([Column("id", int), Column("value", float)]),
        "threshold": float
    },
    outputs={
        "result": Schema([Column("id", int), Column("filtered_value", float)])
    }
)
def filter_values(df, threshold):
    result = df[df["value"] > threshold]
    return {"result": result}

Available Validators

The package comes builtin with many Validators you can use.

• IsPositive: Ensures numeric values are positive
• IsNonEmptyString: Ensures strings are non-empty
• Max: Ensures values are less than or equal to a maximum
• Min: Ensures values are greater than or equal to a minimum
• GreaterThan: Ensures values are greater than a threshold
• GreaterThanOrEqual: Ensures values are greater than or equal to a threshold
• LessThan: Ensures values are less than a threshold
• LessThanOrEqual: Ensures values are less than or equal to a threshold
• Choice: Ensures values are in a list of allowed choices
• Length: Ensures values have a specific length or length range
• Range: Ensures values are within a range

Schema Inference

You can infer a schema from an existing DataFrame:

df = pd.DataFrame({
    "id": [1, 2, 3],
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35]
})

schema = Schema.infer_schema(df)

License

This project is licensed under the MIT License - see the LICENSE file for details.