statsmodels.graphics.gofplots.qqplot_2samples¶

statsmodels.graphics.gofplots.
qqplot_2samples
(data1, data2, xlabel=None, ylabel=None, line=None, ax=None)[source]¶ QQ Plot of two samples’ quantiles.
Can take either two ProbPlot instances or two arraylike 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 finergrained control of the quantile computations is required.
Parameters: data1, data2 : arraylike (1d) or ProbPlot instances
xlabel, ylabel : str or None
Userprovided labels for the xaxis and yaxis. If None (default), other values are used.
line : str {‘45’, ‘s’, ‘r’, q’} or None
Options for the reference line to which the data is compared:
 ‘45’  45degree line
 ‘s’  standardized line, the expected order statistics are scaled by the standard deviation of the given sample and have the mean added to them
 ‘r’  A regression line is fit
 ‘q’  A line is fit through the quartiles.
 None  by default no reference line is added to the plot.
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being created.
Returns: fig : Matplotlib figure instance
If ax is None, the created figure. Otherwise the figure to which ax is connected.
See also
scipy.stats.probplot
Notes
 Depends on matplotlib.
 If data1 and data2 are not ProbPlot instances, instances will be created using the default parameters. Therefore, it is recommended to use ProbPlot instance if finegrained control is needed in the computation of the quantiles.
Examples
>>> x = np.random.normal(loc=8.5, scale=2.5, size=37) >>> y = np.random.normal(loc=8.0, scale=3.0, size=37) >>> pp_x = sm.ProbPlot(x) >>> pp_y = sm.ProbPlot(y) >>> qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None):