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Normalize and scale numeric columns using min-max scaling, z-score standardization, or robust scaling. Supports saving and applying scaler parameters for consistent train/test normalization.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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Export pandas DataFrames or processed datasets to well-formatted CSV files with configurable encoding, delimiter, quoting, and column ordering.
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Comprehensive data cleaning toolkit — handles missing values, duplicate removal, type coercion, whitespace trimming, and outlier detection in a single pass over the dataset.
Identify and remove duplicate records from tabular datasets using exact-match, fuzzy-match, or composite-key-based deduplication strategies.
| name | numeric-normalizer |
| description | Normalize and scale numeric columns using min-max scaling, z-score standardization, or robust scaling. Supports saving and applying scaler parameters for consistent train/test normalization. |
| version | 0.2.3 |
Applies numeric normalization/scaling transformations to specified columns in a tabular dataset. Supports min-max scaling (0-1 range), z-score standardization (mean=0, std=1), and robust scaling (median-centered, IQR-scaled). Can save fitted scaler parameters to a JSON file for later re-application to new data (useful for ML train/test consistency).
x_scaled = (x - min) / (max - min) → range [0, 1]x_scaled = (x - mean) / std → mean 0, std 1x_scaled = (x - median) / IQR → robust to outliersx_scaled = log(x + 1) → for right-skewed distributionsx_scaled = x / max(|x|) → range [-1, 1]--input / -i: Path to input file (CSV or Parquet) (required)--columns / -c: Numeric columns to normalize (comma-separated; default: all numeric)--method / -m: Normalization method (default: min-max)--output / -o: Output file path--save-params: Save scaler parameters to JSON file--load-params: Load previously saved parameters (transform-only mode)scaler_params.json (optional): Fitted parameters per columnpython3 ./skills/numeric_normalizer/normalize.py -i <file> -o <output> -m <method>pandas, numpy, scikit-learn