API Reference

API Reference

Core Class

• StatClean(df: DataFrame, preserve_index: bool = True)
  • set_data, set_thresholds, get_thresholds, reset, get_summary_report

Detection (non-destructive)

• detect_outliers_iqr(column, lower_factor=None, upper_factor=None) → Series
• detect_outliers_zscore(column, threshold=None) → Series
• detect_outliers_modified_zscore(column, threshold=None) → Series
• detect_outliers_mahalanobis(columns=None, chi2_threshold=None, use_shrinkage=False) → Series

Removal / Winsorizing (chained)

• remove_outliers_iqr(column, ...) → self
• remove_outliers_zscore(column, threshold=None) → self
• remove_outliers_modified_zscore(column, threshold=None) → self
• remove_outliers_mahalanobis(columns=None, chi2_threshold=None, use_shrinkage=False) → self
• winsorize_outliers_iqr(column, ...) → self
• winsorize_outliers_zscore(column, threshold=None) → self
• winsorize_outliers_percentile(column, lower_percentile=5, upper_percentile=95) → self

Analysis & Utilities

• analyze_distribution(column) → dict (skewness, kurtosis, normality, recommendation)
• compare_methods(columns=None, methods=None, ...) → dict summary
• get_outlier_stats(columns=None, methods=['iqr','zscore'], ...) → DataFrame
• plot_outlier_analysis(columns=None, methods=None, figsize=(15,5)) → dict[str, Figure]
• visualize_outliers(column) → None

Transformations

• transform_boxcox(column, lambda_param=None) → (self, info)
• transform_log(column, base='natural') → (self, info)
• transform_sqrt(column) → (self, info)
• recommend_transformation(column) → dict

Utils (module statclean.utils)

• plot_outliers(series, outliers_mask, title=None)
• plot_distribution(series, outliers_mask=None, title=None)
• plot_boxplot(series, title=None)
• plot_qq(series, outliers_mask=None, title=None)
• plot_outlier_analysis(data, outliers=None)

Notes:

• Remover methods return self for chaining; access data via cleaner.clean_df.
• Mahalanobis supports percentile thresholds and shrinkage covariance.

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