Frequency distributions and graphing

[DanPatterson_Retired]

A simple verbose demo again, so that I don't forget. 

I updated it to conform to python 3.x... which will inevitably hit ArcMap.

The script is pretty self explanatory and no effort has been made to simplify it.

""" 
CollectionsDemo.py 
Author:   Dan.Patterson@carleton.ca 
Purpose: 
  To demonstrate the utility of using the collections module to 
  obtain a simple frequency distribution, in this case, a list 
  of random integers.  It is written verbosely so that the user 
  can see the sequence of events and the results of the various 
  methods. 
  A sequence of random numbers is generated and a dictionary of 
  key:values is produced by collections.Counter.  The resultant 
  keys are cloned to "classes" to prevent alteration of the 
  initial keys.  An extra class is appended to the list to ensure 
  that the last class in the keys is included since the behaviour 
  of histogram is to combine the last two classes into 
  one frequency (long story).  I just add a value of 1 to the last 
  class to produce an extra bin. 
  A histogram is produced which contains the classes and the frequency 
  for those classes. 
 
"""  
import collections  
import random  
import numpy as np  
from matplotlib import pyplot as plt  
  
rand_int = [random.randrange(1,6) for i in range(15)]  
dict = collections.Counter(rand_int)  
keys = dict.keys()  
counts = dict.values()  
classes = list(keys)                #clone the keys  
classes.append(classes[-1] + 1)  #to ensure that the last bin has values  
#  
histo = np.histogram(rand_int,classes)
#
args = [rand_int, dict, keys, counts, histo, histo[1], histo[0]]  
frmt = """
Collections and pylab
Random integers: {} 
Collections dict:
     {}
  keys:           {}
  values (freq):  {}  
Histogram         {} 
   classes:       {} 
   frequency:     {} 
"""
print(frmt.format(*args))
#
plt.hist(rand_int,bins=classes,align='left')  
plt.title("Sample Histogram", loc='center')  
plt.xlabel("class"); plt.ylabel("frequency")  
plt.show()
plt.close()‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍

Results

Collections and pylab
Random integers: [4, 5, 3, 4, 2, 4, 1, 5, 4, 5, 4, 5, 2, 2, 3] 
Collections dict:
     Counter({4: 5, 5: 4, 2: 3, 3: 2, 1: 1})
  keys:           dict_keys([1, 2, 3, 4, 5])
  values (freq):  dict_values([1, 3, 2, 5, 4])  
Histogram         (array([1, 3, 2, 5, 4]), array([1, 2, 3, 4, 5, 6])) 
   classes:       [1 2 3 4 5 6] 
   frequency:     [1 3 2 5 4] ‍‍‍‍‍‍‍‍‍

As a simple histogram.

Which of course can be fancied up to suit your needs.  Matplotlib is certainly one package to explore... and there are even high-end graphics modules.