Department of Computer Science and Software Engineering
CITS1401 Computational
Thinking with Python
Project 1: Computing World Happiness Index
Submission deadline: 11:59pm, Monday 29 April 2019.
Value: 15% of CITS1401.
To be done individually.
You should construct a Python 3 program containing your solution to the following problem and submit your
program electronically using cssubmit. No other method of submission is allowed.
You are expected to have read and understood the University’s guidelines on academic conduct. In accordance with
this policy, you may discuss with other students the general principles required to understand this project, but the
work you submit must be the result of your own effort. Plagiarism detection, and other systems for detecting
potential malpractice, will therefore be used. Besides, if what you submit is not your own work then you will have
learnt little and will therefore, likely, fail the final exam.
You must submit your project before the submission deadline listed above. Following UWA policy, a late penalty of
10% will be deducted for each day (or part day), after the deadline, that the assignment is submitted. However, in
order to facilitate marking of the assignments in a timely manner, no submissions will be allowed after 7 days
following the deadline.
Overview
For the last few years, the United Nations Sustainable Development Solutions Network has been publishing the World
Happiness Report. Details of the 2018 report can be found here. The underlying data, which you can also download from the
latter URL, is a combination of data from specially commissioned surveys undertaken by the Gallup organisation, and
statistical and economic data from other sources. The web site linked above also provides the methodology for how the
different data have been combined to compute the final score, most dramatically called the Life Ladder.
Here is a sample:
country Life Ladder Log GDP
per capita
Social
support
Healthy life
expectancy
at birth
Freedom to
make life
choices
Generosity Confidence
in national
government
Afghanistan 2.66171813 7.460143566 0.490880072 52.33952713 0.427010864 -0.106340349 0.261178523
Albania 4.639548302 9.373718262 0.637698293 69.05165863 0.74961102 -0.035140377 0.457737535
Algeria 5.248912334 9.540244102 0.806753874 65.69918823 0.436670482 -0.194670126
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Argentina 6.039330006 9.843519211 0.906699121 67.53870392 0.831966162 -0.186299905 0.305430293
Armenia 4.287736416 9.034710884 0.697924912 65.12568665 0.613697052 -0.132166177 0.246900991
Australia 7.25703764 10.71182728 0.949957848 72.78334045 0.910550177 0.301693261 0.45340696
The data shown above (and discussed below) can be found in the CSV formated text file
WHR2018Chapter2_reduced_sample.csv.
The actual method used to compute the Life Ladder score is quite complicated, so the the aim of this Project, in brief, is to
test whether simpler methods can yield similar results. In particular, the Project aims to see whether any of a range of
proposed methods yields a similar ranking, when countries are ranked by Life Ladder score in descending order i.e. from
happiest on these measures, to least happy. (The Wikipedia article also discusses criticisms of the World Happiness Report
process.)
Looking at the data sample above, you can see that the column headers occupy the first row, the countries are listed in the
first column, while the Life Ladder scores that we are seeking to emulate are in the second column. The third and subsequent
columns contain the data from which you will compute your own Life Ladder scores. However, for this exercise, please
remember that the aim is not to replicate the precise Life Ladder scores, but rather to replicate the ranking of countries as a
result of the Life Ladder scores.
Eye-balling the Data
In Data Science projects, it is always a good idea to “eyeball” the data before you attempt to analyse it. The aim is to spot
any trends (“this looks interesting”) or any issues. So, looking at the sample above (ignoring the first two columns), what do
you notice?
There is a difference in scale across the columns. Healthy Life Expectancy at Birth ranges from 52.3 to 72.8, but in
general is valued in 10’s, while Social Support is a value in the range 0.0 to 1.0, and Freedom to Make Life Choices has
both negative and positive floating point numbers. (The problem of GDP per Capita being actually valued in the
thousands, or tens of thousands, has already been solved by the data collectors taking logs.) The issue is that you don’t
want a particular attribute to appear significant just because it has much larger values than other attributes.
The other thing you may have noticed is that sometimes the data is simply missing, e.g. the score for Confidence in
National Government for Algeria. Any metric we propose will have to deal with such missing data (which is actually a
very common problem).
Specification: What your program will need to do
Input
Your program needs to call the Python function input three times to:
get the name of the input data file
get the name of the metric to be computed across the normalised data for each country. The allowed names are “min”,
“mean”, “median” and “harmonic_mean”.
get the name of the action to be performed. The two options here are: “list”, list the countries in descending order of the
computed metric, or “correlation”, use Spearman’s rank correlation coefficient to compute the correlation between ranks
according to the computed metric and the ranks according to the Life Ladder score.
The order of the 3 calls is clearly important.
Output
The output, printed to standard output, will be either a listing of the countries in descending order based on the computed
metric, or a statement containing the correlation value (a number between -1.0 and 1.0).
Tasks: A more detailed specification
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Use input to read in 3 strings, representing the input file name, the metric to be applied to the data from the file
(excluding the first two columns) and the action to be taken to report to the user.
Read in the CSV formated text file. That is, fields in each row are separated by commas, e.g.
Albania,4.639548302,9.373718262,0.637698293,69.05165863,0.74961102,-0.035140377,0
.457737535
Algeria,5.248912334,9.540244102,0.806753874,65.69918823,0.436670482,-0.194670126,
Apart from the first field, all the other fields are either numbers (so converted using float(), or empty, which can be
translated to the Python object None. Each line will be transformed into a row, represented as a list, so you end up with a
list of lists.
For each column apart from the first two, compute the largest and smallest values in the column (ignoring any None
values).
Given the maximum and minimum values for each column, normalise all the values in the respective columns. That is,
each value should be normalised by transforming it to a value between 0.0 and 1.0, where 0.0 corresponds to the smallest
value, and 1.0 to the largest, with other values falling somewhere between 0.0 and 1.0. For example, the minimum Life
Expectancy years in the small dataset is 52.33952713. This is transformed to 0.0. The maximum value is 72.78334045,
which is transformed to 1.0. So, working proportionally, 69.05165863 is transformed to 0.81746645. In general, the
transformation is (score – min)/(max-min), where max and min are the respective maximum and minimum scores for a
given column, and will, of course, differ from column to column.
For each row, across all the columns except the first two, compute the nominated metric using the normalised values
(excluding None). “min”, “mean” and “median” are, respectively, the minimum value (on the basis that a nation’s
happiness is bounded by the thing the citizens are grumpiest about), “mean” and “median” are the arithmetic mean and
median value (discussed in lectures). The harmonic mean of a list of numbers is defined here. For harmonic mean, apart
from avoiding None values, you will also have to avoid any zeroes; the other metrics have no problem with 0. The output
from this stage is a list of country,score pairs.
The list of country,score pairs are either to be listed in order of descending score, or the Spearman’s rank correlation
coefficient should be computed between the country,score list that you have computed and the Life Ladder list, when
sorted by descending score. You can assume there are no tied ranks, which means that the simpler form of the Spearman
calculation can be used. An example of how to compute Spearman’s rank correlation can be found here.
Example
happiness.main()
Enter name of file containing World Happiness computation data:
WHR2018Chapter2_reduced_sample.csv
Choose metric to be tested from: min, mean, median, harmonic_mean mean
Chose action to be performed on the data using the specified metric. Options are
list, correlation correlation
The correlation coefficient between the study ranking and the ranking using the
mean metric is 0.8286
happiness.main()
Enter name of file containing World Happiness computation data:
WHR2018Chapter2_reduced_sample.csv
Choose metric to be tested from: min, mean, median, harmonic_mean harmonic_mean
Chose action to be performed on the data using the specified metric. Options are
list, correlation list
Ranked list of countries’ happiness scores based the harmonic_mean metric
Australia 0.9965
Albania 0.5146
Armenia 0.3046
Afghanistan 0.0981
Argentina 0.0884
Algeria 0.0733
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The complete table is in file WHR2018Chapter2_reduced.csv.
Important
You will have noticed that you have not been asked to write specific functions. That has been left to you. However, it is
important that your program defines the top-level function main(). The idea is that within main() the program calls
the other functions, as described above. (Of course, these may call further functions.) The reason this is important is that
when I test your program, my testing program will call your main() function. So, if you fail to define main(), my
program will not be able to test your program.
Assumptions
Your program can assume a number of things:
Anything is that meant to be a string (i.e. a name) will be a string, and anything that is meant to be a number (i.e. a score
for a country) will be a number.
The order of columns in each row will follow the order of the headings, though data in particular columns may be missing
in some rows.
What being said, there are number of error conditions that your program should explicitly test for and respond to. One
example is detecting whether the named input file exists; for example, the user may have mistyped the name. The way this
test can be done is to first:
import os
Then, assuming the file name is in variable input_filename, use the test:
if not os.path.isfile(input_filename) :
return(None)
and test for None in the calling function (likely main()).
Things to avoid
There are a couple things for your program to avoid.
Please do not use Python’s csv module. While use of the csv module is a perfectly sensible thing to do in a production
setting, it takes away from much of the point of the first part of the project, which is about getting practice opening text
files and processing text file data.
Please do not assume that the input file names will end in .csv. File name suffixes such as .csv and .txt are not mandatory
in systems other than Microsoft Windows.
Please make sure your program has only 3 calls to the input() function. More than 3 will cause your program to hang,
waiting for input that my automated testing system will not provide. In fact, what will happen is that the marking program
detects the multiple calls, and will not test your code at all.
Submission
Submit a single Python (.py) file containing all of your functions via cssubmit.
Marking Rubric
For convenience, your program will be marked out of 20 (later scaled to be out of 15% of the final mark).
60% of the marks (12/20) will be awarded based on how well your program completes a number of tests, reflecting normal
use of the program, and also how the program handles various error states, such as the input file not being present. Other
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than things that you were asked to assume, you need to think creatively about the inputs your program may face.
40% (8/20) will be style (5/8) — the code is clear to read — and efficiency (3/8) — your program is well constructed and
runs efficiently. For style, think about use of comments, sensible variable names, your name at the top of the program.
(Please look at your lecture notes, where this is discussed.)
Style Rubric
0 Gibberish, impossible to understand
1-2 Style is really poor
3-4 Style is good or very good, with small lapses
5 Excellent style, really easy to read and follow
For Project 1, there are not too many ways your code can be inefficient, but try to minimise the number of times your
program looks at the same data items. There are particular places where you should use readline(), but not in a loop.
Efficiency Rubric
0 Code too incomplete to judge efficiency, or wrong problem tackled
1 Very poor efficiency, addtional loops, inappropriate use of readline()
2 Acceptable efficiency, one or more lapses
3 Good efficiency, within the scope of the assignment and where the class is up to
Automated testing is being used so that all submitted programs are being tested the same way. Sometimes it happens that
there is one mistake in the program that means that no tests are passed. If the marker is able to spot the cause and fix it
readily, then they are allowed to do that and your – now fixed – program will score whatever it scores from the tests, minus
2 marks, because other students will not have had the benefit of marker intervention. Still, that’s way better than getting
zero, right? (On the other hand, if the bug is too hard to fix, the marker needs to move on to other submissions.)
WX：codehelp