DataFrame-level

Exploration Methods

FrameOnExplore.info()	Generates a styled overview table with key statistics about the DataFrame.
FrameOnExplore.anomalies_report([...])	Generates a comprehensive report for rows containing specified anomalies across ALL columns.
FrameOnExplore.detect_anomalies([...])	Unified method to detect different types of anomalies by column.
FrameOnExplore.anomalies_corr_matrix(...[, ...])	Advanced correlation matrix showing only columns with anomalies
FrameOnExplore.anomalies_combinations([n, ...])	Unified method to analyze co-occurring anomalies in column combinations.
FrameOnExplore.anomalies_by_categories([...])	Unified method to analyze rows containing specified anomalies across ALL columns by categories.
FrameOnExplore.detect_simultaneous_anomalies(columns)	Detect and analyze rows with simultaneous anomalies of specified type in multiple columns.
FrameOnExplore.anomalies_over_time(time_column)	Plot anomalies across ALL columns over time using resampling.

Preprocessing Methods

FrameOnPreproc.impute_missing(target_cols, ...)	Perform missing value imputation on specified numerical columns.
FrameOnPreproc.restore_full_index(date_cols, ...)	Restores a full index for a DataFrame by filling in missing dates and categories.

Analysis Methods

FrameOnAnalysis.cohort(user_id_col, date_col)	Enhanced cohort analysis with multiple metrics and professional features.
FrameOnAnalysis.corr_matrix([method, ...])	Builds an advanced correlation matrix for numeric columns in the dataframe.
FrameOnAnalysis.rfm(user_id_col, date_col, ...)	Perform comprehensive RFM (Recency, Frequency, Monetary) analysis with visualization.
FrameOnAnalysis.segment_polar(metrics, ...)	Creates a combined visualization showing segment distribution (left) and metric profiles (right).
FrameOnAnalysis.metric_by_dimensions_plot(...)	Creates faceted horizontal bar charts for a metric across multiple dimensions.
FrameOnAnalysis.sentiment(text_column[, ...])	Perform comprehensive sentiment analysis on text data with multiple visualization options.
FrameOnAnalysis.word_frequency(text_column)	Generate bar plots showing the most/least frequent words in a text column.

Visualization Methods

FrameOnViz.bar([x, y, color, ...])	Creates a bar chart using the Plotly Express library.
FrameOnViz.line([x, y, color, ...])	Creates a line chart using the Plotly Express library.
FrameOnViz.area([x, y, color, ...])	Creates an area chart using the Plotly Express library.
FrameOnViz.box([x, y, color, ...])	Creates a box plot using Plotly Express.
FrameOnViz.heatmap([x, y, z, do_pivot, ...])	Creates an enhanced heatmap visualization with support for data aggregation, filtering, and advanced customization.
FrameOnViz.pairplot([pairs, ranges, ...])	Create an advanced pairplot of numerical variables with multiple display options.
FrameOnViz.pie_bar(x, y[, agg_func, ...])	Creates a combined pie and bar chart visualization.
FrameOnViz.histogram([x, y, ...])	Creates a histogram chart using the Plotly Express library.
FrameOnViz.cat_compare(cat1, cat2[, ...])	Compare two categorical variables in a DataFrame and visualize the results.
FrameOnViz.wordcloud(text_column[, title, ...])	Generate an interactive word cloud visualization using Plotly.
FrameOnViz.parallel_categories(dimensions[, ...])	Create a parallel categories plot with customizable colors and category filtering.
FrameOnViz.period_change(metric_col, date_col)	Plot period-over-period changes for a given metric using pd.Grouper with enhanced customization.

Statistics Methods

FrameOnStats.normality(dv, between[, alpha, ...])	Perform normality tests for each group and display QQ-plots.
FrameOnStats.levene(dv, between[, alpha, ...])	Perform Levene's test for equality of variances.
FrameOnStats.ttest(dv, between[, alpha, ...])	Perform comprehensive independent samples t-test analysis with step-by-step reporting.
FrameOnStats.mwu(dv, between[, alpha, ...])	Perform Mann-Whitney U test (non-parametric alternative to t-test) with comprehensive reporting.
FrameOnStats.anova(dv, between[, alpha, ...])	Perform ANOVA analysis with automatic variance checking and appropriate post-hoc tests.
FrameOnStats.kruskal(dv, between[, alpha, ...])	Perform Kruskal-Wallis H-test (non-parametric alternative to one-way ANOVA).
FrameOnStats.chi2_independence(x, y[, test, ...])	Categorical variables association report using pingouin.chi2_independence
FrameOnStats.bootstrap(dv[, between, ...])	Perform bootstrap analysis on DataFrame columns with parallel and non-parallel modes.
FrameOnStats.ols(formula[, cov_type, ...])	Fit Ordinary Least Squares (OLS) regression.
FrameOnStats.rlm(formula[, show_summary, ...])	Fit Robust Linear Model (RLM) using M-estimators.
FrameOnStats.glm(formula[, family, ...])	Fit Generalized Linear Model (GLM).
FrameOnStats.quantreg(formula[, q, ...])	Fit Quantile Regression (median by default).
FrameOnStats.ordered_model(formula[, distr, ...])	Fit an ordinal regression model (Proportional Odds Model).
FrameOnStats.mixedlm(formula, groups[, ...])	Fit Linear Mixed Effects Model.
FrameOnStats.feature_importance_analysis(...)	Advanced feature importance analysis using multiple methods and models.