A4: Asmt#4: Work survey data analysis

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This programming exercise looks at work survey data analysis. There are many types of such surveys in comparing groups.

customer satisfaction between regions
team win-loss percentages
baseball batting averages
... and many more ....

Write a Python program to do the following.

Write a Python program to input the data and output the results as shown in the examples. If the requirements are unclear in any way, use any provided input and output to resolve the discrepancy.

The following coding examples and/or notes may be of use for this requirement.

Note that there are many ways to do the requirements in Python.

Here are some restrictions.

All input is from standard input and output to standard output.
The program file name must be worksurvey.

The output for one or more inputs is not provided. You should insure that your program creates the proper output for those inputs.

Make all necessary assumptions. Make no unnecessary assumptions.

In addition to specific coding examples, you should be familiar with all concepts covered to this point in the course.

You are provided with the following starter program in file worksurvey.py.

Do not remove any comment that starts with two hash signs.

Here is the Python code.

## File: worksurvey.py
## Purpose: Work survey data analysis
## Author: (your name)
## Help received:
# (add here)
## pseudo-code:
# (add here)

import sys
import json

##:BEGIN ToDo

##:END ToDo

You are to fill in the missing parts of the program according to the work requirements (see above).

In the comments at the top, you are to fill in the author (your name), help received (person and type of help), and pseudo-code parts which are not in the solution (usually provided, but you need to add them). Remove the parentheses too.

Begin solution

Here is a possible solution to the above problem. Note: Once a solution is provided, or the day before the next class, further submissions for this work receive no credit.

## File: worksurvey.py
## Purpose: Work survey data analysis
## Author: (your name)
## Help received:
# (add here)
## pseudo-code:
# (add here)

import sys
import json

##:BEGIN ToDo
text1 = sys.stdin.read()

dataDict1 = json.loads(text1)
title1 = dataDict1["title"]
rowsList1 = dataDict1["rows"]
colsList1 = dataDict1["cols"]
groupsList1 = dataDict1["groups"]
dataList1 = dataDict1["data"]
totalList1 = []
for pos1, dataList2 in enumerate(dataList1):
	for pos2, dataList3 in enumerate(dataList2):
		if pos1 == 0:

			totalList2 = []
			totalList1.append(totalList2)

		for pos3, data4 in enumerate(dataList3):
			if pos1 == 0:

				totalList2.append(data4)
			else:

				totalList1[pos2][pos3] += data4
dataList1.append(totalList1)

print("{0:s}".format(title1))
for pos1, group1 in enumerate(groupsList1):
	dataList2 = dataList1[pos1]
	print("")
	print("{0:s}".format(group1))
	lineList1 = []
	lineList1.append("\t")
	for col1 in colsList1:
		lineList1.append("{0:>6}".format(col1))
		lineList1.append(" ")
	lineText1 = "".join(lineList1)
	print("")
	print("{0:s}".format(lineText1))
	name4 = ""
	avg4 = 0.0
	for pos2, dataList3 in enumerate(dataList2):
		name3 = rowsList1[pos2]
		yes3 = dataList3[0]
		no3 = dataList3[1]
		both3 = yes3 + no3
		avg3 = float(yes3) / float(both3)
		print("\t{0:6d} {1:6d} {2:6d} {3:6.3f} {4:s}".format(yes3,no3,both3,avg3,name3))
		if avg3 > avg4:

			name4 = name3
			avg4 = avg3
	print("")
	print("\tBest: {0:s}".format(name4))
##:END ToDo

End solution

Here are some examples of input and output for the above program code.

Here is an example input from file input1.txt.

{
"title": "Batting average analysis",
"rows" : ["Dijkstra","Strawberry"],
"cols" : ["Hits","Outs","Bats", "Avg", "Player"],
"groups" : [ "First half of season", "Second half of season", "Entire season"],
"data": [
	[[65,115], [78,122]],
	[[60,144], [70,156]]
	]
}

For the above example input, here is the expected output.

Batting average analysis

First half of season

	  Hits   Outs   Bats    Avg Player 
	    65    115    180  0.361 Dijkstra
	    78    122    200  0.390 Strawberry

	Best: Strawberry

Second half of season

	  Hits   Outs   Bats    Avg Player 
	    60    144    204  0.294 Dijkstra
	    70    156    226  0.310 Strawberry

	Best: Strawberry

Entire season

	  Hits   Outs   Bats    Avg Player 
	   125    259    384  0.326 Dijkstra
	   148    278    426  0.347 Strawberry

	Best: Strawberry

Here is an example input from file input2.txt.

{
"title": "Customer car satisfaction",
"rows" : ["Toyota","Ford","Honda"],
"cols" : ["Yes","No","All", "Avg", "Car company"],
"groups" : [ "Philadelphia", "Pittsburgh", "Harrisburg", "Cities surveyed"],
"data": [
	[[525,612], [452,525], [582,601]],
	[[487,556], [329,425],[485,532]],
	[[357,516], [289,386],[589,634]]
	]
}

For the above example input, here is the expected output.

Customer car satisfaction

Philadelphia

	   Yes     No    All    Avg Car company 
	   525    612   1137  0.462 Toyota
	   452    525    977  0.463 Ford
	   582    601   1183  0.492 Honda

	Best: Honda

Pittsburgh

	   Yes     No    All    Avg Car company 
	   487    556   1043  0.467 Toyota
	   329    425    754  0.436 Ford
	   485    532   1017  0.477 Honda

	Best: Honda

Harrisburg

	   Yes     No    All    Avg Car company 
	   357    516    873  0.409 Toyota
	   289    386    675  0.428 Ford
	   589    634   1223  0.482 Honda

	Best: Honda

Cities surveyed

	   Yes     No    All    Avg Car company 
	  1369   1684   3053  0.448 Toyota
	  1070   1336   2406  0.445 Ford
	  1656   1767   3423  0.484 Honda

	Best: Honda

Here is an example input from file input3.txt.

{
"title": "Batting average analysis",
"rows" : ["Hank Aaron","Willie Mays"],
"cols" : ["Hits","Outs","Bats", "Avg", "Player"],
"groups" : [ "First half of season", "Second half of season", "Entire season"],
"data": [
	[[40,60], [78,122]],
	[[60,144], [29,71]]
	]
}

For the above example input, here is the expected output.

The output for this input is not provided. Insure your program works for this input.

10. Scoring rubric

CS 496 - A4 : Asmt#4: Work survey data analysis Your grade: _ / 30 [LATE] Late or redo penalty: _ / -30 [SUBMIT] Not submitted properly: _ / -30 [RUN] Does not compile, run-time crash, etc.: _ / -30 [ETHICS] Ethical issues: _ / -30 [HEADER] Header comments not done as required: _ / -15 [STYLE] Inconsistant style or indentation: _ / -15 [CODE] Code guidelines ignored: _ / -30 [XCODE] Code used not yet covered: _ / -15 [REMOVED] Triple slash comments removed: _ / -15 [ADDED] Code added outside of marked areas: _ / -15 [OUTPUT] Functional requirements met: _ / 30 [CREDIT] Extra credit: _ / +6 Comments:

11. End of page

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