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How can I calculate the cumulative distribution function of a normal distribution in python without using scipy?
I'm specifically referring to this function:
from scipy.stats import norm
norm.cdf(1.96)
I have a Django app running on Heroku and getting scipy up and running on Heroku is quite a pain. Since I only need this one function from scipy, I'm hoping I can use an alternative. I'm already using numpy and pandas, but I can't find the function in there. Are there any alternative packages I can use or even implement it myself?
798
Given a random variable X, its cdf is the function F(x) = Prob(X <= x) where the variable x runs through the real numbers. The distribution is called continuous if F(x) is the integral from -infinity to x of a function f called the density function.
A CDF or cumulative distribution function plot is basically a graph with on the X-axis the sorted values and on the Y-axis the cumulative distribution.
Just use math.erf:
import math
def normal_cdf(x):
"cdf for standard normal"
q = math.erf(x / math.sqrt(2.0))
return (1.0 + q) / 2.0
Edit to show comparison with scipy:
scipy.stats.norm.cdf(1.96)
# 0.9750021048517795
normal_cdf(1.96)
# 0.9750021048517796
This question seems to be a duplicate of How to calculate cumulative normal distribution in Python where there are many alternatives to scipy listed.
I wanted to highlight the answer of Xavier Guihot https://stackoverflow.com/users/9297144/xavier-guihot which shows that from python3.8 the normal is now a built in:
from statistics import NormalDist
NormalDist(mu=0, sigma=1).cdf(1.96)
# 0.9750021048517796
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