How do I calculate PDF (probability density function) in Python?

Unless you have a reason to implement this yourself. All these functions are available in scipy.stats.norm

I think you asking for the cdf, then use this code:

from scipy.stats import normprint(norm.cdf(x, mean, std))

If you want to write it from scratch:

class PDF():    def __init__(self,mu=0, sigma=1):        self.mean = mu        self.stdev = sigma        self.data = []    def calculate_mean(self):        self.mean = sum(self.data) // len(self.data)        return self.mean    def calculate_stdev(self,sample=True):        if sample:            n = len(self.data)-1        else:            n = len(self.data)        mean = self.mean        sigma = 0        for el in self.data:            sigma += (el - mean)**2        sigma = math.sqrt(sigma / n)        self.stdev = sigma        return self.stdev    def pdf(self, x):        return (1.0 / (self.stdev * math.sqrt(2*math.pi))) * math.exp(-0.5*((x - self.mean) / self.stdev) ** 2)

The area under a curve y = f(x) from x = a to x = b is the same as the integral of f(x)dx from x = a to x = b. Scipy has a quick easy way to do integrals. And just so you understand, the probability of finding a single point in that area cannot be one because the idea is that the total area under the curve is one (unless MAYBE it's a delta function). So you should get 0 ≤ probability of value < 1 for any particular value of interest. There may be different ways of doing it, but a conventional way is to assign confidence intervals along the x-axis like this. I would read up on Gaussian curves and normalization before continuing to code it.