Graphics#

Goodness of Fit Plots#

`qqplot`(data[, dist, distargs, a, loc, ...])	Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution
`qqline`(ax, line[, x, y, dist, fmt])	Plot a reference line for a qqplot
`qqplot_2samples`(data1, data2[, xlabel, ...])	Q-Q Plot of two samples' quantiles
`ProbPlot`(data[, dist, fit, distargs, a, ...])	Q-Q and P-P Probability Plots

`violinplot`(data[, ax, labels, positions, ...])	Make a violin plot of each dataset in the data sequence
`beanplot`(data[, ax, labels, positions, ...])	Bean plot of each dataset in a sequence

`plot_corr`(dcorr[, xnames, ynames, title, ...])	Plot correlation of many variables in a tight color grid
`plot_corr_grid`(dcorrs[, titles, ncols, ...])	Create a grid of correlation plots

scatter_ellipse(data[, level, varnames, ...])

Create a grid of scatter plots with confidence ellipses

dot_plot(points[, intervals, lines, ...])

Dot plotting (also known as forest and blobbogram)

`hdrboxplot`(data[, ncomp, alpha, threshold, ...])	High Density Region boxplot
`fboxplot`(data[, xdata, labels, depth, ...])	Plot functional boxplot
`rainbowplot`(data[, xdata, depth, method, ...])	Create a rainbow plot for a set of curves
`banddepth`(data[, method])	Calculate the band depth for a set of functional curves

`plot_fit`(results, exog_idx[, y_true, ax, vlines])	Plot fit against one regressor
`plot_regress_exog`(results, exog_idx[, fig])	Plot regression results against one regressor
`plot_partregress`(endog, exog_i, exog_others)	Plot partial regression for a single regressor
`plot_partregress_grid`(results[, exog_idx, ...])	Plot partial regression for a set of regressors
`plot_ccpr`(results, exog_idx[, ax])	Plot CCPR against one regressor
`plot_ccpr_grid`(results[, exog_idx, grid, fig])	Generate CCPR plots against a set of regressors, plot in a grid
`plot_ceres_residuals`(results, focus_exog[, ...])	Conditional Expectation Partial Residuals (CERES) plot
`abline_plot`([intercept, slope, horiz, vert, ...])	Plot a line given an intercept and slope
`influence_plot`(results[, external, alpha, ...])	Plot of influence in regression.
`plot_leverage_resid2`(results[, alpha, ax])	Plot leverage statistics vs. normalized residuals squared.

`plot_acf`(x[, ax, lags, alpha, use_vlines, ...])	Plot the autocorrelation function
`plot_pacf`(x[, ax, lags, alpha, method, ...])	Plot the partial autocorrelation function
`plot_ccf`(x, y, *[, ax, lags, negative_lags, ...])	Plot the cross-correlation function
`plot_pccf`(x, y, *[, ax, lags, method, ...])	Plot the partial cross-correlation function
`plot_accf_grid`(x, *[, varnames, fig, lags, ...])	Plot auto/cross-correlation grid
`month_plot`(x[, dates, ylabel, ax])	Seasonal plot of monthly data
`quarter_plot`(x[, dates, ylabel, ax])	Seasonal plot of quarterly data

interaction_plot(x, trace, response[, func, ...])

Interaction plot for factor level statistics

mosaic(data[, index, ax, horizontal, gap, ...])

Create a mosaic plot from a contingency table

mean_diff_plot(m1, m2[, sd_limit, ax, ...])

Construct a Tukey/Bland-Altman Mean Difference Plot