Quantile functions in Python

Question

I'm having trouble finding quantile functions for well-known probability distributions in Python, do they exist? In particular, is there an inverse normal distribution function? I couldn't find anything in either Numpy or Scipy.

... and http://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p/20627638#20627638 — Warren Weckesser, Aug 10 '15 at 02:03

score 6 · Accepted Answer · answered Jun 29 '18 at 08:56

Check the .ppf() method of any distribution class in scipy.stats. This is the equivalent of a quantile function (otherwise named as percent point function or inverse CDF)

An example with the exponential distribution from scipy.stats:

# analysis libs
import scipy
import numpy as np
# plotting libs
import matplotlib as mpl
import matplotlib.pyplot as plt

# Example with the exponential distribution
c = 0
lamb = 2

# Create a frozen exponential distribution instance with specified parameters
exp_obj = scipy.stats.expon(c,1/float(lamb))

x_in = np.linspace(0,1,200) # 200 numbers in [0,1], input for ppf()
y_out = exp_obj.ppf(x_in)
plt.plot(x_in,y_out) # graphically check the results of the inverse CDF

score 0 · Answer 2 · answered Aug 10 '18 at 08:47

0

It seems new but I've found this about numpy and quantile. Maybe you can have a look (not tested)

answered Aug 10 '18 at 08:47

MaxD

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That gives the empirical quantiles of a set of observations, rather than the exact quantiles of a theoretical distribution the poster is asking for. It is new to numpy, but gives the same functionality as the function `np.percentile`. – user2699 Aug 10 '18 at 13:13

Quantile functions in Python

2 Answers2