statsmodels.graphics.plot_grids.scatter_ellipse statsmodels.graphics.plot_grids.scatter_ellipse(data, level=0.9, varnames=None, ell_kwds=None, plot_kwds=None, add_titles=False, keep_ticks=False, fig=None) [source] Create a grid of scatter plots with confidence ellipses. ell_kwds, plot_kdes not used yet looks ok with 5 or 6 variables, too crowded with 8, too empty with 1 Parameters: data : array_like Input data. level : scalar, optional Default is 0.9. varnames : list of str, optional V

statsmodels.graphics.mosaicplot.mosaic statsmodels.graphics.mosaicplot.mosaic(data, index=None, ax=None, horizontal=True, gap=0.005, properties= at 0x2ac652f5b6e0>, labelizer=None, title='', statistic=False, axes_label=True, label_rotation=0.0) [source] Create a mosaic plot from a contingency table. It allows to visualize multivariate categorical data in a rigorous and informative way. Parameters: data : dict, pandas.Series, np.ndarray, pandas.DataFrame The contingency table that contai

statsmodels.graphics.gofplots.qqplot_2samples statsmodels.graphics.gofplots.qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None) [source] Q-Q Plot of two samples? quantiles. Can take either two ProbPlot instances or two array-like objects. In the case of the latter, both inputs will be converted to ProbPlot instances using only the default values - so use ProbPlot instances if finer-grained control of the quantile computations is required. Parameters: data1, data2 : a

statsmodels.graphics.gofplots.qqplot statsmodels.graphics.gofplots.qqplot(data, dist=, distargs=(), a=0, loc=0, scale=1, fit=False, line=None, ax=None) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.dis

statsmodels.graphics.gofplots.ProbPlot class statsmodels.graphics.gofplots.ProbPlot(data, dist=, fit=False, distargs=(), a=0, loc=0, scale=1) [source] Class for convenient construction of Q-Q, P-P, and probability plots. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under kwargs.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.distributions.norm (a

statsmodels.graphics.gofplots.qqline statsmodels.graphics.gofplots.qqline(ax, line, x=None, y=None, dist=None, fmt='r-') [source] Plot a reference line for a qqplot. Parameters: ax : matplotlib axes instance The axes on which to plot the line line : str {?45?,?r?,?s?,?q?} Options for the reference line to which the data is compared.: ?45? - 45-degree line ?s? - standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean a

statsmodels.graphics.functional.fboxplot statsmodels.graphics.functional.fboxplot(data, xdata=None, labels=None, depth=None, method='MBD', wfactor=1.5, ax=None, plot_opts={}) [source] Plot functional boxplot. A functional boxplot is the analog of a boxplot for functional data. Functional data is any type of data that varies over a continuum, i.e. curves, probabillity distributions, seasonal data, etc. The data is first ordered, the order statistic used here is banddepth. Plotted are then the

statsmodels.graphics.functional.rainbowplot statsmodels.graphics.functional.rainbowplot(data, xdata=None, depth=None, method='MBD', ax=None, cmap=None) [source] Create a rainbow plot for a set of curves. A rainbow plot contains line plots of all curves in the dataset, colored in order of functional depth. The median curve is shown in black. Parameters: data : sequence of ndarrays or 2-D ndarray The vectors of functions to create a functional boxplot from. If a sequence of 1-D arrays, these

statsmodels.graphics.functional.banddepth statsmodels.graphics.functional.banddepth(data, method='MBD') [source] Calculate the band depth for a set of functional curves. Band depth is an order statistic for functional data (see fboxplot), with a higher band depth indicating larger ?centrality?. In analog to scalar data, the functional curve with highest band depth is called the median curve, and the band made up from the first N/2 of N curves is the 50% central region. Parameters: data : nd

statsmodels.graphics.factorplots.interaction_plot statsmodels.graphics.factorplots.interaction_plot(x, trace, response, func=, ax=None, plottype='b', xlabel=None, ylabel=None, colors=[], markers=[], linestyles=[], legendloc='best', legendtitle=None, **kwargs) [source] Interaction plot for factor level statistics. Note. If categorial factors are supplied levels will be internally recoded to integers. This ensures matplotlib compatiblity. uses pandas.DataFrame to calculate an aggregate statist