Quantile-Quantile Plot using SciPy

Question

How would you create a qq-plot using Python?

Assuming that you have a large set of measurements and are using some plotting function that takes XY-values as input. T

Answer 1

To add to the confusion around Q-Q plots and probability plots in the Python and R worlds, this is what the SciPy manual 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."

If you try out scipy.stats.probplot, you'll see that indeed it compares a dataset to a theoretical distribution. Q-Q plots, OTOH, compare two datasets (samples).

R has functions qqnorm, qqplot and qqline. From the R help (Version 3.6.3):

qqnorm is a generic function the default method of which produces a normal QQ plot of the values in y. qqline adds a line to a “theoretical”, by default normal, quantile-quantile plot which passes through the probs quantiles, by default the first and third quartiles.

qqplot produces a QQ plot of two datasets.

In short, R's qqnorm offers the same functionality that scipy.stats.probplot provides with the default setting dist=norm. But the fact that they called it qqnorm and that it's supposed to "produce a normal QQ plot" may easily confuse users.

Finally, a word of warning. These plots don't replace proper statistical testing and should be used for illustrative purposes only.