问题
I have the following list:
[6, 4, 0, 0, 0, 0, 0, 1, 3, 1, 0, 3, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3, 2, 3, 3, 2, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 3, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0, 0, 1, 2]
I want to plot the frequency of each entity with python and make a powerlaw analysis on it.
But I cannot figure how I can plot the list with ylabel the frequency and xlabel the numbers on the list.
I thought to create a dict with the frequencies and plot the values of the dictionary, but with that way, I cannot put the numbers on xlabel.
Any advice?
回答1:
I think you're right about the dictionary:
>>> import matplotlib.pyplot as plt
>>> from collections import Counter
>>> c = Counter([6, 4, 0, 0, 0, 0, 0, 1, 3, 1, 0, 3, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3, 2, 3, 3, 2, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 3, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0, 0, 1, 2])
>>> sorted(c.items())
[(0, 50), (1, 30), (2, 9), (3, 8), (4, 1), (5, 1), (6, 1)]
>>> plt.plot(*zip(*sorted(c.items()))
... )
[<matplotlib.lines.Line2D object at 0x36a9990>]
>>> plt.show()
There are a few pieces here that are of interest. zip(*sorted(c.items())) will return something like [(0,1,2,3,4,5,6),(50,30,9,8,1,1,1)]. We can unpack that using the * operator so that plt.plot sees 2 arguments -- (0,1,2,3,4,5,6) and (50,30,9,8,1,1,1). which are used as the x and y values in plotting respectively.
As for fitting the data, scipy will probably be of some help here. Specifically, have a look at the following examples. (one of the examples even uses a power law).
回答2:
Use the package: powerlaw
import powerlaw
d=[6, 4, 0, 0, 0, 0, 0, 1, 3, 1, 0, 3, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3,2, 3, 3, 2, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1,0, 1, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,3, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0, 0, 1, 2]
fit = powerlaw.Fit(numpy.array(d)+1,xmin=1,discrete=True)
fit.power_law.plot_pdf( color= 'b',linestyle='--',label='fit ccdf')
fit.plot_pdf( color= 'b')
print('alpha= ',fit.power_law.alpha,' sigma= ',fit.power_law.sigma)
alpha= 1.85885487521 sigma= 0.0858854875209
It allow to plot, fit and analyse the data correctly. It has as special method for fit on power law distributions with discrete data.
it can be installed with: pip install powerlaw
回答3:
y = np.bincount([6, 4, 0, 0, 0, 0, 0, 1, 3, 1, 0, 3, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3, 2, 3, 3, 2, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 3, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0, 0, 1, 2])
x = np.nonzero(y)[0]
plt.bar(x,y)
回答4:
import matplotlib.pyplot as plt
data = [6, 4, 0, 0, 0, 0, 0, 1, 3, 1, 0, 3, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 3, 2, 3, 3, 2, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 3, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 2, 3, 2, 1, 0, 0, 0, 1, 2]
plt.hist(data, bins=range(max(data)+2))
plt.show()
来源：https://stackoverflow.com/questions/16640496/python-plot-and-powerlaw-fit