Here's some pure-Python code you can use to calculate the mean and standard deviation.
All code below is based on the statistics module in Python 3.4+.
def mean(data):
"""Return the sample arithmetic mean of data."""
n = len(data)
if n < 1:
raise ValueError('mean requires at least one data point')
return sum(data)/n # in Python 2 use sum(data)/float(n)
def _ss(data):
"""Return sum of square deviations of sequence data."""
c = mean(data)
ss = sum((x-c)**2 for x in data)
return ss
def stddev(data, ddof=0):
"""Calculates the population standard deviation
by default; specify ddof=1 to compute the sample
standard deviation."""
n = len(data)
if n < 2:
raise ValueError('variance requires at least two data points')
ss = _ss(data)
pvar = ss/(n-ddof)
return pvar**0.5
Note: for improved accuracy when summing floats, the statistics module uses a custom function _sum rather than the built-in sum which I've used in its place.
Now we have for example:
>>> mean([1, 2, 3])
2.0
>>> stddev([1, 2, 3]) # population standard deviation
0.816496580927726
>>> stddev([1, 2, 3], ddof=1) # sample standard deviation
0.1