schemaguard 0.1.0

Lightweight DataFrame Schema Validator

schemaguard

Lightweight DataFrame Schema Validator for Python

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About

schemaguard is a lightweight Python library that allows you to define and validate schemas for Pandas DataFrames. It helps ensure your data meets expected formats, types, and constraints before entering production pipelines, saving you time and reducing errors.

With schemaguard, you can:

• Define column types, nullability, uniqueness, regex patterns, and numeric bounds.
• Validate Pandas DataFrames against YAML or Python-based schemas.
• Version your schemas to track changes over time.
• Integrate easily into ETL pipelines, Airflow, or CI/CD workflows.

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Features

• Column type validation (int, float, str, etc.)
• Null check (not_null)
• Uniqueness check (unique)
• Regex pattern matching (regex)
• Numeric bounds (min_value, max_value)
• Schema versioning
• YAML-based schema loader
• Lightweight and fast, ideal for small and large datasets.

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Installation

You can install schemaguard via pip:

pip install schemaguard

Or install from source for development:

git clone https://github.com/inzamam1121/schemaguard.git
cd schemaguard
pip install -e .

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Quick Start

1. Define a YAML schema

Create files/schema_v1.yml:

version: "1.0"

columns:
  order_id:
    type: int
    not_null: true
    unique: true

  amount:
    type: float
    not_null: true
    min: 0
    max: 10000

  email:
    type: string
    regex: "^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$"

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2. Validate your DataFrame

import pandas as pd
from schemaguard.loader.yaml_loader import load_schema_from_yaml

# Load schema
schema = load_schema_from_yaml("files/schema_v1.yml")

# Load data
df = pd.read_csv("files/data.csv")

# Validate
report = schema.validate(df, raise_on_error=False)

# Print report
print(report)

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3. Sample Output

{
  'ok': False,
  'errors': {
    'amount': 'null values present',
    'email': 'regex mismatch',
    'order_id': 'duplicate values found'
  },
  'version': '1.0'
}

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Versioning

schemaguard uses semantic versioning:

• 0.1.0 — Initial release with YAML schema loader and basic validators.
• Future releases will add more validators, performance improvements, and integration examples.

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Use Cases

• Validate incoming ETL data before storing in warehouse.
• Ensure API payloads match expected schema.
• Quickly enforce data contracts in collaborative teams.
• Detect and prevent data quality issues in production.

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Author

Inzamam Yousaf Email: [e-mail] GitHub: https://github.com/inzamam1121

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License

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

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Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork the repository and submit pull requests.