Statistics related stuff in igraph
Class 

Result of fitting a powerlaw to a vector of samples 
Class 

Generic histogram class for real numbers 
Class 

Running mean calculator. 
Function  mean 
Returns the mean of an iterable. 
Function  median 
Returns the median of an unsorted or sorted numeric vector. 
Function  percentile 
Returns the pth percentile of an unsorted or sorted numeric vector. 
Function  power 
Fitting a powerlaw distribution to empirical data 
Function  quantile 
Returns the qth quantile of an unsorted or sorted numeric vector. 
Function  sd 
Returns the standard deviation of an iterable. 
Function  var 
Returns the variance of an iterable. 
Returns the mean of an iterable.
Example:
>>> mean([1, 4, 7, 11]) 5.75
Parameters  
xs  an iterable yielding numbers. 
Returns  
the mean of the numbers provided by the iterable.  
See Also  
RunningMean() if you also need the variance or the standard deviation 
Returns the median of an unsorted or sorted numeric vector.
Parameters  
xs  the vector itself. 
sort  whether to sort the vector. If you know that the vector is sorted already, pass False here. 
Returns  
the median, which will always be a float, even if the vector contained integers originally. 
Returns the pth percentile of an unsorted or sorted numeric vector.
This is equivalent to calling quantile(xs, p/100.0); see quantile
for more details on the calculation.
Example:
>>> round(percentile([15, 20, 40, 35, 50], 40), 2) 26.0 >>> for perc in percentile([15, 20, 40, 35, 50], (0, 25, 50, 75, 100)): ... print("%.2f" % perc) ... 15.00 17.50 35.00 45.00 50.00
Parameters  
xs  the vector itself. 
p  the percentile we are looking for. It may also be a list if you want to calculate multiple quantiles with a single call. The default value calculates the 25th, 50th and 75th percentile. 
sort  whether to sort the vector. If you know that the vector is sorted already, pass False here. 
Returns  
the pth percentile, which will always be a float, even if the vector contained integers originally. If p is a list, the result will also be a list containing the percentiles for each item in the list. 
Fitting a powerlaw distribution to empirical data
Parameters  
data  the data to fit, a list containing integer values 
xmin  the lower bound for fitting the powerlaw. If None, the optimal xmin value will be estimated as well. Zero means that the smallest possible xmin value will be used. 
method  the fitting method to use. The following methods are implemented so far:

Returns  
a FittedPowerLaw object. The fitted xmin value and the powerlaw exponent can be queried from the xmin and alpha properties of the returned object.  
Unknown Field: newfield  
ref  Reference 
Unknown Field: ref  
MEJ Newman: Power laws, Pareto distributions and Zipf's law. Contemporary Physics 46, 323351 (2005)  
A Clauset, CR Shalizi, MEJ Newman: Powerlaw distributions in empirical data. Eprint (2007). arXiv:0706.1062 
Returns the qth quantile of an unsorted or sorted numeric vector.
There are a number of different ways to calculate the sample quantile. The method implemented by igraph is the one recommended by NIST. First we calculate a rank n as q(N+1), where N is the number of items in xs, then we split n into its integer component k and decimal component d. If k <= 1, we return the first element; if k >= N, we return the last element, otherwise we return the linear interpolation between xs[k1] and xs[k] using a factor d.
Example:
>>> round(quantile([15, 20, 40, 35, 50], 0.4), 2) 26.0
Parameters  
xs  the vector itself. 
q  the quantile we are looking for. It may also be a list if you want to calculate multiple quantiles with a single call. The default value calculates the 25th, 50th and 75th percentile. 
sort  whether to sort the vector. If you know that the vector is sorted already, pass False here. 
Returns  
the qth quantile, which will always be a float, even if the vector contained integers originally. If q is a list, the result will also be a list containing the quantiles for each item in the list. 
Returns the standard deviation of an iterable.
Example:
>>> sd([1, 4, 7, 11]) #doctest:+ELLIPSIS 4.2720...
Parameters  
xs  an iterable yielding numbers. 
Returns  
the standard deviation of the numbers provided by the iterable.  
See Also  
RunningMean() if you also need the mean 