This is a script I have written to calculate the population standard deviation. I feel that this can be simplified and also be made more pythonic.

from math import sqrt



def mean(lst):

    """calculates mean"""

    sum = 0

    for i in range(len(lst)):

        sum += lst[i]

    return (sum / len(lst))



def stddev(lst):

    """calculates standard deviation"""

    sum = 0

    mn = mean(lst)

    for i in range(len(lst)):

        sum += pow((lst[i]-mn),2)

    return sqrt(sum/len(lst)-1)



numbers = [120,112,131,211,312,90]



print stddev(numbers)

The easiest way to make mean() more pythonic is to use the sum() built-in function.

def mean(lst):

    return sum(lst) / len(lst)

Concerning your loops on lists, you don't need to use range(). This is enough:

for e in lst:

   sum += e

Other comments:

• You don't need parentheses around the return value (check out PEP 8 when you have a doubt about this).


• Your docstrings are useless: it's obvious from the name that it calculates the mean. At least make them more informative ("returns the mean of lst").


• Why do you use "-1" in the return for stddev? Is that a bug?


• You are computing the standard deviation using the variance: call that "variance", not sum!


• You should type pow(e-mn,2), not pow((e-mn),2). Using parentheses inside a function call could make the reader think he's reading a tuple (eg. pow((e,mn),2) is valid syntax)


• You shouldn't use pow() anyway, ** is enough.

This would give:

def stddev(lst):

    """returns the standard deviation of lst"""

    variance = 0

    mn = mean(lst)

    for e in lst:

        variance += (e-mn)**2

    variance /= len(lst)



    return sqrt(variance)

It's still way too verbose! Since we're handling lists, why not using list comprehensions?

def stddev(lst):

    """returns the standard deviation of lst"""

    mn = mean(lst)

    variance = sum([(e-mn)**2 for e in lst]) / len(lst)

    return sqrt(variance)

This is not perfect. You could add tests using doctest. Obviously, you should not code those functions yourself, except in a small project. Consider using Numpy for a bigger project.

You have some serious calculation errors…

Assuming that this is Python 2, you also have bugs in the use of division: if both operands of / are integers, then Python 2 performs integer division. Possible remedies are:

• from __future__ import division


• Cast one of the operands to a float: return (float(sum)) / len(lst), for example.

(Assuming that this is Python 3, you can just use statistics.stdev().

The formula for the sample standard deviation is

$$ s = sqrt{frac{sum_{i=1}^{n} (x_i - bar{x})^2}{n - 1}}$$

In return sqrt(sum/len(lst)-1), you have an error with the precedence of operations. It should be

return sqrt(float(sum) / (len(lst) - 1))

Does not work, Example:
import scipy.stats as ss
import numpy as np
x = np.arange(-5,5,0.01) # X AXIS VALUES
y = ss.norm(0.0, 1.0).pdf(x) #Generate Normally Distributed values mu = 0.0, sigma = 1.0
print("The Std. Dev. of y =", np.std(y))

Result =

The Std. Dev. of y = 0.13494254572180692 #Shoud be approx = 1.0 by definition of y