edf.assess()

Compute a structured data-quality assessment for a dataset.

Description

Measures completeness, uniqueness and the package's quality dimensions for a supported file or pandas DataFrame. The returned AssessmentReport includes dataset information, recommendations, validations and export helpers for HTML, JSON, Markdown, CSV, Excel and PDF.

Parameters

Returns

Raises

• FileNotFoundError — the supplied path does not exist.
• ValueError — the supplied file type is not supported.

Examples

import eazydatafix as edf

report = edf.assess("employees.csv")

print(report.dataset_info.rows, report.dataset_info.columns)
print(report.completeness.total_missing_values)
print(report.uniqueness.duplicate_rows)
print(report.quality.score, report.quality.grade)

>>> report = edf.assess("employees.csv")
>>> report.dataset_info.rows, report.dataset_info.columns
(12, 8)
>>> report.completeness.total_missing_values
5
>>> report.quality.score
82.76

report = edf.assess("employees.csv")
report.to_html("quality.html")
report.to_json("quality.json")

Notes

• assess() never mutates its input; call fix() to obtain a cleaned copy.
• The quality score is available at report.quality.score, not report.quality_score.

Best Practices

• Run assess() before and after controlled cleaning to measure the effect.
• Use a report export method when you need a durable CI or audit artefact.

See Also