7.16. Graphics¶
7.16.1. 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. |
7.16.2. Boxplots¶
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. |
7.16.3. 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. |
7.16.4. Functional Plots¶
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. |
7.16.5. 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. |
regressionplots.plot_ccpr(results, exog_idx) |
Plot CCPR against one regressor. |
regressionplots.abline_plot([intercept, ...]) |
Plots a line given an intercept and slope. |
regressionplots.influence_plot(results[, ...]) |
Plot of influence in regression. |
regressionplots.plot_leverage_resid2(results) |
Plots leverage statistics vs. |
7.16.6. 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 |
7.16.7. 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. |