How to interpret scipy.stats.probplot results?

Question

I wanted to use scipy.stats.probplot() to perform some gaussianity test on mydata.

from scipy import stats
_,fit=stats.probplot(mydata, dist=stats.norm,plot=ax)
goodness_fit="%.2f" %fit[2]

The documentation says:

Generates a probability plot of sample data against the quantiles of a specified theoretical distribution (the normal distribution by default). probplot optionally calculates a best-fit line for the data and plots the results using Matplotlib or a given plot function. probplot generates a probability plot, which should not be confused with a Q-Q or a P-P plot. Statsmodels has more extensive functionality of this type, see statsmodels.api.ProbPlot.

But if google probability plot, it is a common name for P-P plot, while the documentation says not to confuse the two things.

Now I am confused, what is this function doing?

Answer 1

I looked since hours for an answer to this question, and this can be found in the Scipy/Statsmodel code comments.

In Scipy, comment at https://github.com/scipy/scipy/blob/abdab61d65dda1591f9d742230f0d1459fd7c0fa/scipy/stats/morestats.py#L523 says:

probplot generates a probability plot, which should not be confused with a Q-Q or a P-P plot. Statsmodels has more extensive functionality of this type, see statsmodels.api.ProbPlot.

So, now, let's look at Statsmodels, where comment at https://github.com/statsmodels/statsmodels/blob/66fc298c51dc323ce8ab8564b07b1b3797108dad/statsmodels/graphics/gofplots.py#L58 says:

ppplot : Probability-Probability plot Compares the sample and theoretical probabilities (percentiles).

qqplot : Quantile-Quantile plot Compares the sample and theoretical quantiles

probplot : Probability plot Same as a Q-Q plot, however probabilities are shown in the scale of the theoretical distribution (x-axis) and the y-axis contains unscaled quantiles of the sample data.

So, difference between QQ plot and Probability plot, in these modules, is related to the scales.