Quick Start
Run the complete v1 workflow, inspect each result, then choose whether to save the cleaned dataset.
1. Install
terminal
bash
pip install eazydatafix==1.4.02. Run the complete workflow
edf.run() profiles the source, measures its quality, applies controlled cleaning and runs deterministic EDA. The result keeps every stage available separately.
quickstart.py
python
1import eazydatafix as edf23result = edf.run("employees.csv")45print(result.profile.rows)6print(result.assessment.quality.score)7print(result.fix_result.applied_fixes)8print(result.eda_result.observations)910result.fix_result.save("employees-clean.csv")3. Preview cleaning before applying it
Use a dry run when you want to inspect every proposed change first. The source dataset remains in preview.dataset; the cleaned proposal is available separately.
preview_cleaning.py
python
1import eazydatafix as edf23preview = edf.fix(4 "employees.csv",5 edf.FixConfig(dry_run=True),6)78print(preview.change_log)9print(preview.proposed_dataset.head())4. Add a data contract
Infer an expected schema from trusted data and validate new inputs before your analysis or model pipeline continues.
validate_input.py
python
1contract = edf.infer_schema("employees.csv")23rules = (4 edf.QualityRule("employee_id_unique", "employee_id", "unique"),5 edf.QualityRule("salary_non_negative", "salary", "min", 0),6)78validation = edf.validate_contract(9 "employees-next.csv",10 contract,11 rules,12)1314assert validation.passed, validation.to_dict()