Goodness of Fit Plots

gofplots.qqplot(data[, dist, distargs, a, …])

Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution.

gofplots.qqline(ax, line[, x, y, dist, fmt])

Plot a reference line for a qqplot.

gofplots.qqplot_2samples(data1, data2[, …])

Q-Q Plot of two samples’ quantiles.

gofplots.ProbPlot(data[, dist, fit, …])

Class for convenient construction of Q-Q, P-P, and probability plots.


boxplots.violinplot(data[, ax, labels, …])

Make a violin plot of each dataset in the data sequence.

boxplots.beanplot(data[, ax, labels, …])

Make a bean plot of each dataset in the data sequence.

Correlation Plots

correlation.plot_corr(dcorr[, xnames, …])

Plot correlation of many variables in a tight color grid.

correlation.plot_corr_grid(dcorrs[, titles, …])

Create a grid of correlation plots.

plot_grids.scatter_ellipse(data[, level, …])

Create a grid of scatter plots with confidence ellipses.

Functional Plots

functional.hdrboxplot(data[, ncomp, alpha, …])

High Density Region boxplot

functional.fboxplot(data[, xdata, labels, …])

Plot functional boxplot.

functional.rainbowplot(data[, xdata, depth, …])

Create a rainbow plot for a set of curves.

functional.banddepth(data[, method])

Calculate the band depth for a set of functional curves.

Regression Plots

regressionplots.plot_fit(results, exog_idx)

Plot fit against one regressor.

regressionplots.plot_regress_exog(results, …)

Plot regression results against one regressor.

regressionplots.plot_partregress(endog, …)

Plot partial regression for a single regressor.


Plot partial regression for a set of regressors.

regressionplots.plot_ccpr(results, exog_idx)

Plot CCPR against one regressor.

regressionplots.plot_ccpr_grid(results[, …])

Generate CCPR plots against a set of regressors, plot in a grid.

regressionplots.plot_ceres_residuals(…[, …])

Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model.

regressionplots.abline_plot([intercept, …])

Plots a line given an intercept and slope.

regressionplots.influence_plot(results[, …])

Plot of influence in regression.


Plots leverage statistics vs.

Time Series Plots

tsaplots.plot_acf(x[, ax, lags, alpha, …])

Plot the autocorrelation function

tsaplots.plot_pacf(x[, ax, lags, alpha, …])

Plot the partial autocorrelation function

tsaplots.month_plot(x[, dates, ylabel, ax])

Seasonal plot of monthly data

tsaplots.quarter_plot(x[, dates, ylabel, ax])

Seasonal plot of quarterly data

Other Plots

factorplots.interaction_plot(x, trace, response)

Interaction plot for factor level statistics.

mosaicplot.mosaic(data[, index, ax, …])

Create a mosaic plot from a contingency table.

agreement.mean_diff_plot(m1, m2[, sd_limit, …])

Tukey’s Mean Difference Plot.