framecheck 0.5.1

Lightweight, readable, and extensible validation for pandas DataFrames.

Lightweight, flexible, and intuitive validation for pandas DataFrames.
Define expectations for your data, validate them cleanly, and surface friendly errors or warnings — no configuration files, ever.

🔗 View full documentation →

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📦 Installation

pip install framecheck

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Try FrameCheck

Try FrameCheck without installing anything - click the badge above to run an interactive demo notebook in Google Colab.

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Main Features

• Designed for pandas users
• Simple, fluent API
• Supports error or warning-level assertions
• Validates both column-level and DataFrame-level rules
• No config files, decorators, or boilerplate

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Why Should You Validate Your Data Explicitly?

Because during a routine update, a typo like this:

SELECT u.credit_score AS age
FROM users u
JOIN profiles p ON u.id = p.user_id

...fails silently.

Now your age column has values in the 700s.

Your SQL doesn't error, your model still runs — and makes terrible predictions.

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🔥 Example: Catch Bad Model Output Before It Hits Production

import pandas as pd
from framecheck import FrameCheck, register_check_function

df = pd.DataFrame({
    'transaction_id': ['TXN1001', 'TXN1002', 'TXN1003'],
    'user_id': [501, 502, 503],
    'transaction_time': ['2024-04-15 08:23:11', '2024-04-15 08:45:22', '2024-04-15 09:01:37'],
    'model_score': [0.03, 0.92, 0.95],
    'model_version': ['v2.1.0', 'v2.1.0', 'v2.1.0'],
    'flagged_for_review': [False, True, False]
})

@register_check_function(name="high_score_is_flagged")
def high_score_is_flagged(row):
    return row['model_score'] <= 0.9 or row['flagged_for_review'] is True

model_score_validator = (
    FrameCheck()
    .column('transaction_id', type='string', regex=r'^TXN\d{4,}$')
    .column('user_id', type='int', min=1)
    .column('transaction_time', type='datetime', before='now')
    .column('model_score', type='float', min=0.0, max=1.0)
    .column('model_score', type='float', not_in_set=[0.0], warn_only=True)
    .column('model_version', type='string')
    .column('flagged_for_review', type='bool')
    .custom_check(
        high_score_is_flagged,
        "flagged_for_review must be True when model_score > 0.9"
    )
    .not_null()
    .not_empty()
    .only_defined_columns()
)

result = model_score_validator.validate(df)

if not result.is_valid:
    print(result.summary())

Output:

Validation FAILED
1 error(s), 1 warning(s)
Errors:
  - flagged_for_review must be True when model_score > 0.9 (failed on 1 row(s))
Warnings:
  - Column 'model_score' contains disallowed values: [0.0].

Identify Problem Records

invalid_rows = result.get_invalid_rows(df, include_warnings = True)

transaction_id	user_id	transaction_time	model_score	model_version	flagged_for_review
TXN1003	503	2024-04-15 09:01:37	0.95	v2.1.0	False

📋 Save & Reuse Validation Rules

model_score_validator.save('transaction_validator.json')

loaded_validator = FrameCheck.load('transaction_validator.json')

result = loaded_validator.validate(df)

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📊 Comparison with Other Tools

FrameCheck is designed to be concise and pandas-native. For full comparisons with other packages:

• Pydantic Comparison
• Pandera Comparison

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License

MIT

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