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

Question

I have the following code below that prints the PDF graph for a particular mean and standard deviation.

http://imgur.com/a/oVgML

Now I need to find the actual probability, of a particular value. So for example if my mean is 0, and my value is 0, my probability is 1. This is usually done by calculating the area under the curve. Similar to this:

http://homepage.divms.uiowa.edu/~mbognar/applets/normal.html

I am not sure how to approach this problem

import numpy as np
import matplotlib    
import matplotlib.pyplot as plt

def normal(power, mean, std, val):
    a = 1/(np.sqrt(2*np.pi)*std)
    diff = np.abs(np.power(val-mean, power))
    b = np.exp(-(diff)/(2*std*std))
    return a*b

pdf_array = []
array = np.arange(-2,2,0.1)
print array
for i in array:
    print i
    pdf = normal(2, 0, 0.1, i)
    print pdf
    pdf_array.append(pdf)

plt.plot(array, pdf_array)
plt.ylabel('some numbers')
plt.axis([-2, 2, 0, 5])
plt.show()

print 

Answer 1

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 norm
print(norm.cdf(x, mean, std))