Skill v1.0.0
currentLLM-judged scan90/100version: "1.0.0" name: completeness description: Calculate data completeness (non-null rate) per variable in a QUIQ-format table. Use for data quality assessment to identify variables with missing values and compute overall completeness ratio. tier: community category: lydus parameters: quiq_path: description: Path to QUIQ-format CSV file (output of quiq skill). type: string save_path: description: Directory path to save output files (completeness_total.txt, completeness_summary.csv). type: string
Completeness
Calculates data completeness for each variable in a QUIQ-format table. Used in the LYDUS quality management program to measure how many records have non-null values for each variable.
When to Use This Skill
- After QUIQ conversion, to assess how complete the data is per variable
- To identify variables with high null rates
- To compute an overall completeness ratio across all variables
Input Requirements
A QUIQ-format CSV with:
Original_table_name,Variable_name— used to group variablesValue— NULL presence determines completeness
Metrics
For each (Original_table_name, Variable_name) group:
| Metric | Formula | |
|---|---|---|
Total_num | Total row count | |
Null_num | Rows where Value is NULL | |
Completeness (%) | (Total_num - Null_num) / Total_num × 100 |
Overall completeness = weighted average across all variables (by Total_num).
Output
| File | Description | |
|---|---|---|
completeness_total.txt | Overall Completeness (%), Total_num, Null_num | |
completeness_summary.csv | Per-variable completeness table |
How to Run
import osimport duckdbskill_dir = os.path.dirname(os.path.abspath(__file__))with open(os.path.join(skill_dir, "scripts/duckdb.sql")) as f:sql = f.read()quiq_csv = "/path/to/quiq_3patients.csv"sql = sql.replace("{quiq_csv}", quiq_csv)df = duckdb.sql(sql).df()total_num = df["Total_num"].sum()null_num = df["Null_num"].sum()completeness = round((total_num - null_num) / total_num * 100, 2)print(f"Completeness (%) = {completeness}")save_path = "/path/to/output"os.makedirs(save_path, exist_ok=True)df.to_csv(f"{save_path}/completeness_summary.csv", index=False, encoding="utf-8-sig")with open(f"{save_path}/completeness_total.txt", "w") as f:f.write(f"Completeness (%) = {completeness}\n")f.write(f"Total Num = {total_num}\n")f.write(f"Null Num = {null_num}\n")print(f"Saved {len(df):,} rows → {save_path}")
As a script with config
# config.yamlquiq_path: /path/to/quiq.csvsave_path: /path/to/output
python scripts/completeness.py --config config.yaml
Critical Notes
- Value 컬럼 기준 — completeness는
Value컬럼의 NULL 여부만 판단. 빈 문자열("")은 non-null로 계산됨.
- 전체 completeness —
completeness_total.txt의 값은 변수별 가중 평균이 아니라, 전체 행 기준 단순 비율 ((전체 - NULL) / 전체 × 100).
References
- LYDUS 품질관리 프로그램 활용 가이드라인 (비공개 내부 문서)
- Original Python implementation: LYDUS_Completeness.py (이성민 작성)