Assessment

What is assessed

• Dataset name, row and column counts, and memory use
• Overall missing-value count and completeness score
• Exact duplicate rows and uniqueness score
• Completeness, uniqueness, validity, consistency, accuracy and timeliness
• Composite quality score and grade
• Recommendations and validation results

Running an assessment

import eazydatafix as edf

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

>>> report.dataset_info.rows, report.dataset_info.columns
(12, 8)
>>> report.completeness.total_missing_values
5
>>> report.quality.score
82.76

Reading structured metrics

Use the report fields directly when a pipeline needs to make a decision:

missing = report.completeness.total_missing_values
duplicates = report.uniqueness.duplicate_rows
score = report.quality.score

if score < 80:
    raise SystemExit(f"quality gate failed: {score}")

Exporting

Export methods write directly to a file. Reports support HTML, JSON, Markdown, CSV, Excel and PDF.

report.to_html("quality.html")
report.to_json("quality.json")
report.to_markdown("quality.md")

Next

Once you know what is wrong, use edf.fix() to apply a controlled cleaning pipeline.