| 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 |
Numeric Normalizer
Skill Summary
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).
Scaling Methods
- min-max:
x_scaled = (x - min) / (max - min) → range [0, 1]
- z-score:
x_scaled = (x - mean) / std → mean 0, std 1
- robust:
x_scaled = (x - median) / IQR → robust to outliers
- log:
x_scaled = log(x + 1) → for right-skewed distributions
- max-abs:
x_scaled = x / max(|x|) → range [-1, 1]
Inputs
--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)
Processing Steps
- Load dataset and identify target columns
- Compute scaling parameters (min/max, mean/std, or median/IQR)
- Apply transformation to each target column
- Optionally save parameters for reproducibility
- Save normalized dataset
Output
- Normalized dataset (CSV or Parquet)
scaler_params.json (optional): Fitted parameters per column- Console: Per-column stats before/after normalization
Implementation
- Command:
python3 ./skills/numeric_normalizer/normalize.py -i <file> -o <output> -m <method>
- Dependencies:
pandas, numpy, scikit-learn
