Create custom plots from a polars DataFrame in Python using the pyrsm library's `visualize` function — a plotnine wrapper supporting 8 geom types (dist / hist / density / scatter / bar / line / box / violin) with full control over aesthetics (color / fill / shape / group / linetype), faceting (facet_wrap / facet_grid), aggregation, smoothing (lm / loess), jitter, titles, and multiple x/y variables. The return value is a `plotnine.ggplot` object for one plot or a plotnine composition for multiple plots, and users can extend the result with plotnine layers — scales, themes, geom_smooth, geom_vline, custom labels — making plotnine extensibility a first-class part of this skill. Triggers include phrases like "scatter plot of x vs y", "histogram of price", "box plot of price by cut", "line chart over time, colored by region", "facet by category", "add a regression line", "use plotnine to plot", "density plot of carat by cut", or any request for a specific plot in a marketing/business analytics context.
Combine two polars DataFrames in Python using the pyrsm library's `combine` function — supporting inner / left / right / full / semi / anti joins, vertical and horizontal binds, and set operations (intersect / union / setdiff). Use this skill whenever a student or analyst wants to merge two tables on a key, stack two tables of the same schema, identify rows that appear in both tables (or in one but not the other), or work with the pyrsm package for any two-DataFrame combining task. The output is a polars DataFrame the user can keep chaining onto. Triggers include phrases like "join these two tables", "merge on customer_id", "left join", "stack two datasets", "concatenate rows", "find rows in A but not in B", "anti join to find unmatched", "set intersection of two tables", "bind columns side by side", or any mention of combining two tabular datasets in a marketing/business analytics context.
Compare means of a numeric variable across two or more groups in Python using the pyrsm library's `compare_means` class. Use this skill whenever a student or analyst wants to run a two-sample t-test, a paired t-test, or an ANOVA-style set of pairwise comparisons; switch to a Wilcoxon rank-sum / signed-rank test for skewed or small samples; apply Bonferroni adjustment to control family-wise error across multiple comparisons; choose between independent and paired samples; or work with the pyrsm package for any group-mean-comparison task — even if they don't explicitly say "pyrsm" or "compare_means". Triggers include phrases like "compare means across groups", "t-test between two groups", "ANOVA across professor ranks", "is salary different by sex/discipline", "before-after paired test", "Wilcoxon rank-sum test", "Bonferroni adjustment", "pairwise comparisons with multiple-testing correction", or any mention of comparing a continuous outcome across categorical groups in a marketing/business analytics class.
Compare proportions of a categorical outcome across two or more groups in Python using the pyrsm library's `compare_props` class. Use this skill whenever a student or analyst wants to test if the proportion of a "success" level (yes / churned / clicked / survived) differs across groups defined by another categorical variable, focus on specific pair comparisons via `comb`, apply Bonferroni adjustment, validate the chi-squared cell-count assumption, or work with the pyrsm package for any group-proportion-comparison task — even if they don't explicitly say "pyrsm" or "compare_props". Triggers include phrases like "compare proportions across groups", "does survival rate differ by class", "did the click-rate change between A and B and C", "two-proportion z-test", "is the yes-rate the same for males and females", "pairwise proportion comparisons with adjustment", or any mention of comparing a binary outcome across two or more categorical groups in a marketing/business analytics class.
Compute and interpret correlation matrices in Python using the pyrsm library's `correlation` class. Use this skill whenever a student or analyst wants to measure the strength and direction of pairwise relationships among numeric variables, choose between Pearson, Spearman, Kendall, or polychoric correlation, build a scatter-matrix visualization with significance stars, screen for multicollinearity in a dataset before fitting a regression, or work with the pyrsm package for any correlation task — even if they don't explicitly say "pyrsm" or "correlation". Triggers include phrases like "how correlated are X and Y", "compute a correlation matrix", "is there a relationship between these variables", "check for multicollinearity", "Pearson vs Spearman", "what's the rank correlation", "scatter matrix with regression lines", or any mention of measuring pairwise linear/monotonic association among continuous variables in a marketing/business analytics class.
Build and interpret two-way contingency tables and chi-squared tests of independence in Python using the pyrsm library's `cross_tabs` class. Use this skill whenever a student or analyst wants to test whether two categorical variables are associated, build observed/expected/chi-squared/standardized-deviation tables, identify which cells drive an omnibus rejection, compute row/column/total percentages, validate the expected-cell-count assumption, or work with the pyrsm package for any contingency-table task — even if they don't explicitly say "pyrsm" or "cross_tabs". Triggers include phrases like "is X associated with Y", "chi-square test of independence", "cross-tab", "contingency table", "does income predict newspaper choice", "which cells contribute to the chi-squared", "standardized residuals", "expected vs observed frequencies", "row percentages vs column percentages", or any mention of analyzing the relationship between two categorical variables in a marketing/business analytics class.
Run distribution analysis on a polars DataFrame in Python using the pyrsm library's `distr` class. Use this skill whenever a student or analyst wants a one-shot exploratory summary of every column in a dataset — numeric variables get mean/median/min/max/sd, categorical variables get counts and proportions per level, other (date/datetime) get min/max/n_unique/n_missing — paired with histograms (numeric) and bar plots (categorical). The class also auto-classifies low-cardinality integers as categorical, and returns plotnine ggplot objects users can layer additional aesthetics onto. Triggers include phrases like "describe this dataset", "show me the distributions", "plot all the variables", "categorical vs numeric split", "histograms for every numeric column", "what's in this DataFrame", "EDA dump", or any request for a holistic first-look at a dataset in a marketing/business analytics context.
Build, validate, solve, and interpret decision trees with pyrsm.model.dtree