In [2]:

    

import numpy as np
from scipy import stats


    

In [3]:

    

array = np.array([1, 1, 5, 0, 1, 2, 2, 0, 1, 4])


    

In [10]:

    

frequency = stats.itemfreq(array)
print(frequency)


    

    




[[0 2]
 [1 4]
 [2 2]
 [4 1]
 [5 1]]

In [12]:

    

frequency.shape


    

    
Out[12]:





(5, 2)

In [15]:

    

bins = frequency.shape[0]


    

In [18]:

    

cumulative_frequency = stats.cumfreq(array, bins)
print(cumulative_frequency)b


    

    




CumfreqResult(cumcount=array([  2.,   6.,   8.,   9.,  10.]), lowerlimit=-0.625, binsize=1.25, extrapoints=0)

In [69]:

    

%matplotlib notebook
import matplotlib.pyplot as plt

x = np.arange(bins)
fig = plt.figure(1)
plt.bar(x, cumulative_frequency.cumcount, width=cumulative_frequency.binsize)
plt.xlim([0, bins+1])
plt.ylim([0, cumulative_frequency.cumcount.max()+1])


    

    















    












    
Out[69]:





(0, 11.0)