Interpretations
 All quantile points lie on or at a straight line at an angle of 45 degrees from the xaxis. This indicates that the two samples have similar distributions.
And in practice it is always impossible to get such a 100% clear straight line, but the graph looks like this. Here the points lie almost on a straight line.
 Yquantiles are below xquantiles. This indicates that the y values tend to be lower than the x values.
And in practice it is not always possible to get 100 percent as shown above, but the graph looks as shown below. Here you can see that most of the points are below the line, and a few points are above the line. Hence, we can say that the distributions are not the same.
 Xquantiles are lower than yquantiles. This indicates that the x values tend to be lower than the y values.
 Indicates that there is a breakpoint up to which the yquantiles are below the xquantiles, and after this point, the yquantiles are higher than the xquantiles.
Quantile — Quantile plot using statsmodel
in Python —
import numpy as np
import statsmodels.api as sm
import pylab as py
data_points = < code class = "plain"> np.random.normal ( 0 , 1 , 100 )
sm.qqplot (data_points, line = `45` )
py.show ()

Output: