For using igraph from Python
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Statistics related stuff in igraph
License: Copyright (C) 20062012 Tamas Nepusz <ntamas@gmail.com> Pázmány Péter sétány 1/a, 1117 Budapest, Hungary This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 021101301 USA


FittedPowerLaw Result of fitting a powerlaw to a vector of samples 

Histogram Generic histogram class for real numbers 

RunningMean Running mean calculator. 

















__package__ =

Imports: math

Returns the mean of an iterable. Example: >>> mean([1, 4, 7, 11]) 5.75
See Also: RunningMean() if you also need the variance or the standard deviation 
Returns the median of an unsorted or sorted numeric vector.

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

Fitting a powerlaw distribution to empirical data
Reference:

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

Returns the standard deviation of an iterable. Example: >>> sd([1, 4, 7, 11]) #doctest:+ELLIPSIS 4.2720...
See Also: RunningMean() if you also need the mean 
Returns the variance of an iterable. Example: >>> var([1, 4, 8, 11]) #doctest:+ELLIPSIS 19.333333...
See Also: RunningMean() if you also need the mean 
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