Frequency distributions and graphing

Blog Post created by Dan_Patterson Champion on Aug 18, 2014

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.


Author:   Dan.Patterson@carleton.ca
  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
  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:     {}

plt.title("Sample Histogram", loc='center') 
plt.xlabel("class"); plt.ylabel("frequency") 




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.