Series-level

Exploration Methods

SeriesOnExplore.info([plot, column_type, ...])	Generate combined report with summary and histogram
SeriesOnExplore.detect_anomalies([...])	Detects anomalies in the series using the specified method.
SeriesOnExplore.detect_outliers([method, ...])	Detect outliers in series using statistical and machine learning methods.
SeriesOnExplore.anomalies_by_categories([...])	Analyze anomaly distribution across all categorical columns in parent DataFrame.
SeriesOnExplore.anomalies_over_time(time_column)	Plot anomalies over time using resampling.
SeriesOnExplore.detect_window_outliers(...)	Detect and analyze outliers in rolling windows of time series data.
SeriesOnExplore.plot_rolling_anomaly_rate(...)	Calculate and visualize the rolling rate of specified anomalies in a time series.

Preprocessing Methods

SeriesOnPreproc.to_categorical([method, ...])	Convert numerical series to categorical using specified method.
SeriesOnPreproc.normalize_string_series([...])	Normalize a pandas Series of strings with comprehensive cleaning and standardization options.
SeriesOnPreproc.transform_numeric([method, ...])	Apply advanced numeric transformations with automatic visualization and skewness handling.
SeriesOnPreproc.fill_missing_by_category(...)	Fill missing values using category-based strategies
SeriesOnPreproc.impute_missing([...])	Perform missing value imputation on specified numerical columns.
SeriesOnPreproc.calc_target_category_share(...)	Calculate the proportional share of a target category within grouped data, with support for time-based resampling and comprehensive data validation.
SeriesOnPreproc.check_group_counts(...[, ...])	Analyze group statistics to assess viability for missing value imputation.