Divide by Zero in Mean()? - python-3.x

Question

I'm trying to write some code to compute mean, Variance, Standard Deviation, FWHM, and finally evaluate the Gaussian Integral. I've been running into a division by zero error that I can't get past and I would like to know the solution for this ?
Where it's throwing an error I've tried to throw an exception handler as follows
Average = (sum(yvalues)) / (len(yvalues)) try: return (sum(yvalues) / len(yvalues))
expect ZeroDivisionError:
return 0
xvalues = []
yvalues = []
def generate():
for i in range(0,300):
a = rand.uniform((float("-inf") , float("inf")))
b = rand.uniform((float("-inf") , float("inf")))
xvalues.append(i)
### Defining the variable 'y'
y = a * (b + i)
yvalues.append(y) + 1
def mean():
Average = (sum(yvalues))/(len(yvalues))
print("The average is", Average)
return Average
def varience():
# This calculates the SD and the varience
s = []
for i in yvalues:
z = i - mean()
z = (np.abs(i-z))**2
s.append(y)**2
t = mean()
v = numpy.sqrt(t)
print("Answer for Varience is:", v)
return v
Traceback (most recent call last):
File "Tuesday.py", line 42, in <module>
def make_gauss(sigma=varience(), mu=mean(), x = random.uniform((float("inf"))*-1, float("inf"))):
File "Tuesday.py", line 35, in varience
t = mean()
File "Tuesday.py", line 25, in mean
Average = (sum(yvalues))/(len(yvalues))
ZeroDivisionError: division by zero

Answer 1

There are a few things that are not quite right as people noted above.
import random
import numpy as np
def generate():
xvalues, yvalues = [], []
for i in range(0,300):
a = random.uniform(-1000, 1000)
b = random.uniform(-1000, 1000)
xvalues.append(i)
### Defining the variable 'y'
y = a * (b + i)
yvalues.append(y)
return xvalues, yvalues
def mean(yvalues):
return sum(yvalues)/len(yvalues)
def variance(yvalues):
# This calculates the SD and the varience
s = []
yvalues_mean = mean(yvalues)
for y in yvalues:
z = (y - yvalues_mean)**2
s.append(z)
t = mean(s)
return t
def variance2(yvalues):
yvalues_mean = mean(yvalues)
return sum( (y-yvalues_mean)**2 for y in yvalues) / len(yvalues)
# Generate the xvalues and yvalues
xvalues, yvalues = generate()
# Now do the calculation, based on the passed parameters
mean_yvalues = mean(yvalues)
variance_yvalues = variance(yvalues)
variance_yvalues2 = variance2(yvalues)
print('Mean {} variance {} {}'.format(mean_yvalues, variance_yvalues, variance_yvalues2))
# Using Numpy
np_mean = np.mean(yvalues)
np_var = np.var(yvalues)
print('Numpy: Mean {} variance {}'.format(np_mean, np_var))
The way variance was calculated isn't quite right, but given the comment of "SD and variance" you were probably going to calculate both.
The code above gives 2 (well, 3) ways to do what I understand you were trying to do but I changed a few of the methods to clean them up a bit. generate() returns two lists now. mean() returns the mean, etc. The function variance2() gives an alternative way to calculate the variance but using a list comprehension style.
The last couple of lines are an example using numpy which has all of it built in and, if available, is a great way to go.
The one part that wasn't clear was the random.uniform(float("-inf"), float("inf"))) which seems to be an error (?).

Answer 2

You are calling mean before you call generate.
This is obvious since yvalues.append(y) + 1 (in generate) would have caused another error (TypeError) since .append returns None and you can't add 1 to None.
Change yvalues.append(y) + 1 to yvalues.append(y + 1) and then make sure to call generate before you call mean.
Also notice that you have the same error in varience (which should be called variance, btw). s.append(y)**2 should be s.append(y ** 2).
Another error you have is that the stacktrace shows make_gauss(sigma=varience(), mu=mean(), x = random.uniform((float("inf"))*-1, float("inf"))).
I'm pretty sure you don't actually want to call varience and mean on this line, just reference them. So also change that line to make_gauss(sigma=varience, mu=mean, x = random.uniform((float("inf"))*-1, float("inf")))