Functions (organizing inline code)

[bratiskovci]

1.How do I get functions to help me simplify access to the data structure.
2. How do I get functions to encapsulate the various calculations that I have to make.
3. How do I get functions to print out the computed information.

import sys
subjects = ["A", "B", "L", "Z", "Q", "T", "V"]

# Trial Data
trials = [["Trial 1", [ 178, 206, 271, 254, 261, 218, 255]],
          ["Trial 2", [ 206, 215, 221, 244, 218, 271, 215]],
          ["Trial 3", [ 237, 298, 215, 233, 224, 216, 195]],
          ["Trial 4", [ 198, 273, 219, 241, 218, 279, 234]],
          ["Trial 5", [ 234, 226, 302, 263, 217, 275, 216]],
          ["Trial 6", [ 217, 256, 227, 227, 299, 234, 229]]
         ]


# Print out trial averages
for trial in trials:
    trialSum = 0.0

    for measures in trial[1]:
        trialSum = trialSum + measures
    print "Average for %s is %.3f mg/dL." % (trial[0], trialSum/len(trial[1]))


# Add spacing between data groups for better visual appearance.
print ""

# print out subject averages
for subject in subjects:
    subjectIndex = subjects.index(subject)
    subjectSum = 0.0

    # compute average for subject over all trials
    for trial in trials:
        subjectSum = subjectSum + trial[1][subjectIndex]

    # Print out average
    sys.stdout.write( "Average for subject %s is %.3f mg/dL.  " %
                      (subject, subjectSum/len(trials)))

    # Classify subject
    if subjectSum/len(trials)>240:
        print "Subject %s is high risk." % subject
    elif subjectSum/len(trials)>200:
        print "Subject %s is borderline high risk." % subject
    else:
        print "Subject %s is low risk." % subject

[bartonc]

This is a great question! With your code organized like this, you will be able to change the way you get your data more easily.

import sys
def GetSubjects():
	return ["A", "B", "L", "Z", "Q", "T", "V"]
def GetTrialData():
	# Trial Data
	return [["Trial 1", [ 178, 206, 271, 254, 261, 218, 255]],
			 ["Trial 2", [ 206, 215, 221, 244, 218, 271, 215]],
			 ["Trial 3", [ 237, 298, 215, 233, 224, 216, 195]],
			 ["Trial 4", [ 198, 273, 219, 241, 218, 279, 234]],
			 ["Trial 5", [ 234, 226, 302, 263, 217, 275, 216]],
			 ["Trial 6", [ 217, 256, 227, 227, 299, 234, 229]]
			 ]
 
def PrintTrailAverage(trials):
	# Print out trial averages
	for trial in trials:
		trialSum = 0.0
		for measures in trial[1]:
			trialSum = trialSum + measures
		print "Average for %s is %.3f mg/dL." % (trial[0], trialSum/len(trial[1]))
	# Add spacing after this data group for better visual appearance.
	print
def PrintSubjectAverage(subjects, trials):
	# print out subject averages
	for subject in subjects:
		subjectIndex = subjects.index(subject)
		subjectSum = 0.0
		# compute average for subject over all trials
		for trial in trials:
			subjectSum = subjectSum + trial[1][subjectIndex]
		# Print out average
		sys.stdout.write( "Average for subject %s is %.3f mg/dL. " %
						 (subject, subjectSum/len(trials)))
		# Classify subject
		if subjectSum/len(trials)>240:
			print "Subject %s is high risk." % subject
		elif subjectSum/len(trials)>200:
			print "Subject %s is borderline high risk." % subject
		else:
			print "Subject %s is low risk." % subject
	# Add spacing after this data group for better visual appearance.
	print
def MainProgram():
	s = GetSubjects()
	td = GetTrialData()
	PrintTrailAverage(td)
	PrintSubjectAverage(s, td)
MainProgram()

Now that you see how it's done, I'll bet you can move the calculations to fuctions... Have fun and keep posting,
Barton