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日期:2019-12-01 09:23

Description Type Name

Cover sheet Compulsory One PDF (.pdf) file Student_number.pdf

Your solution to question 1 Compulsory One Python (.py) file Q1.py

Your solution to question 2 Compulsory One Python (.py) file Q2.py

Your solution to question 3 Compulsory One Python (.py) file Q3.py

For question 1 and 2 submission follow below instructions:

Download the following files from Learning Central:

? Q1.py

? Q2.py

Replace ‘Student_number’ by your student number, e.g. C1234567890. Make sure to include

your student number as a comment in all of the Python files!

Submission Instructions

Your coursework should be submitted via Learning Central by the above deadline. You have

to upload the following files:

? Q3.py

? IEEEexample.docx

? APAexample.docx

? nobelprizes.json

? whitelist.txt

Test your implementation:

For Q1 and Q2, you can execute the function from the command line

> python3 Q1.py

> python3 Q2.py

You can change the parameters of the function call in the main part of Q1.py and Q2.py.

For Q3, you can use

> python3 Q3.py my_paper.docx IEEE

where my_paper.docx is an example journal paper and IEEE represents the target style.

Any deviation from the submission instructions above (including the number and types of

files submitted) will result in a mark of zero for the assessment or question part.

Staff reserve the right to invite students to a meeting to discuss coursework submissions

Assignment

Answer all of the following questions.

Question 1 – Random converter

Implement a function random_converter(x) that takes a variable x. It then returns the value

of x that has been randomly converted into int, float, bool, string or complex.

For instance, for x = 12 (an integer) random_converter(x) can return ’12’ (a string) or 12.0

(a float).

Further instructions:

? x can be any type in int, float, bool, string or complex.

? the assignment needs to be truly random, that is, if repeated several times, different

outcomes should result.

? If x cannot be converted (e.g. the string “house” cannot be converted to a number) the

function should print “cannot be converted” and return none.

As a starting point, use Q1.py from Learning Central. Do not rename the file or the function.

Question 2.1

- Implement a function report(), which takes as input the json file loaded as a Python

dictionary (which is the default data structure returned by the json.load() method). This

function should return a Pandas DataFrame, where you include the years and categories in

which a Nobel Prize was awarded and those in which it was not. You are not expected to

infer any missing information, you should only include years and categories for which there

is an explicit entry in the original dataset. The result should be of the following form (made

up values):

year category awarded_or_not

1963 chemistry True

1976 physics False

Further instructions:

? There is no field called ‘awarded_or_not’ in the dataset, you have to find this

information elsewhere. Discuss your solution in the code as comments.

? Years should be represented as integers, categories as strings and awarded_or_not

values should be boolean.

? Column names should be ‘year’, ‘category’ and ‘awarded_or_not’.

Question 2.2

- Write a function get_laureates_and_motivation() which takes as input three

arguments: the nobel prize dictionary (same as in Q2.1), year (a string) and category (a

string). This function returns a Pandas DataFrame containing one row per laureate (i.e., a

person who has won the Nobel prize). The returned DataFrame should be of the form below

(made up values):

category year id laureate motivation overall_motiva

tion

chemistry 1963 501 john doe he was great he was among

great minds

chemistry 1963 700 susan sarandon she was great NaN

Further instructions:

? The id values refer to the laureate id as per their identifier in the original dataset.

? Overall motivations are reasons for awarding Nobel prizes which apply to more than

one person in the same batch. However, not all laureates have an overall motivation

associated. In those cases, you should insert a NaN value in their ‘overall_motivation’

field.

? Categories, laureates and motivation should be strings, years and ids should be

integers, and overall_motivation should be either string or NaN.

Question 2 – Nobel Prize Data Mining

You are provided with a dataset in json format (nobelprizes.json), which contains information

about Nobel prize winners. Specifically, you will find information about a winner’s name,

category, reason for award, year, etc. To load the dataset, you will need to use the json

module (import json), and the d = json.load(file_object) method.

? Use the column names shown in the sample table above, do not change them.

Question 2.3

- Write a function plot_freqs() which generates six plots, one for each category. The xaxis

should contain the 1st, 10th, 20th, 30th, 40th and 50th most frequent word across the

motivation sections for each category. The y-axis should refer to the frequency of each word

in that category. The resulting plot should have a similar arrangement as the one below.

Further instructions.

? You should only count the words provided in whitelist.txt, a text file available in

learning central, with one word per line. Do not count others.

? Your figures should have a title, legend, the frequency of each word, tick marks, labels

in the x axis for each word, be readable (e.g., big enough fonts), etc.

As a starting point, use Q2.py from Learning Central. Do not rename the file or the functions.

Question 3 – Citation Style Manager

In scientific publications, a reference to a previous work (source) that is discussed in the

manuscript is called a citation. In different scientific disciplines, and sometimes even

different journals, different so-called citation styles are used. The citation style defines how a

citation is formatted. We will consider two different citation styles in this question:

- APA style: citation style of the American Psychological Association

(https://www.mendeley.com/guides/apa-citation-guide), see also Wikipedia page

(https://en.wikipedia.org/wiki/APA_style). This style is widely used in Psychology

and Social Sciences.

- IEEE: citation style of the Institute for Electrical and Electronics Engineers (IEEE) is

used in IEEE journals which cover engineering and related disciplines

(https://pitt.libguides.com/citationhelp/ieee). See the Learning Materials/Coursework

folder on Learning Central for more information on the IEEE style.

There are two main aspects to a publication where citation styles apply:

1. In-text citations: These are used in the text body whenever one refers to, summarises,

paraphrases, or quotes from another source. This is an example from Wikipedia

(https://en.wikipedia.org/wiki/APA_style) for a sentence including an in-text citation

of a paper by Schmidt and Oh in APA format:

In our postfactual era, many members of the public fear that the findings of

science are not real (Schmidt & Oh, 2016).

In IEEE format, references are given as numbers in square brackets. Example:

This is compounded by the fact that the field is evolving from work performed

by an individual that does data science to a team that does data science [1].

2. Reference list: In a scientific publication, the last section is typically the References

section, which provides full details on the in-text citations. For instance, the full

reference corresponding to the Schmidt & Oh (2016) in-text citation above would be:

Schmidt, F. L., & Oh, I.-S. (2016). The crisis of confidence in research

findings in psychology: Is lack of replication the real problem? Or is it

something else? Archives of Scientific Psychology, 4(1), 32–37.

https://doi.org/10.1037/arc0000029

In an article using IEEE format, every reference in the reference list needs to be

numbered:

1. J. Saltz, "The Need for New Processes Methodologies and Tools to Support

Big Data Teams and Improve Big Data Project Effectiveness", Big Data

Conference, 2015.

Your task: Implement a function change_style(filepath, style), which takes as input

two arguments: (1) filepath, which can be either IEEEexample.docx or APAexample.docx

and (2) style (a string being either IEEE or APA), and swaps their citation style (i.e.,

converts IEEE citations into APA and vice versa). You are not expected to consider cases

outside the two documents provided.

Detail instructions:

? To ease the task, you will be working with .docx files (working with PDFs or online

sources would be more difficult). Two example files (IEEEexample.docx and

APAexample.docx) are provided in Learning Central.

? Use the python-docx package to read, manipulate, and save doc files. You can install

it using e.g. pip install python-docx. Check the webpage (https://pythondocx.readthedocs.io/en/latest/index.html#)

or other online sources to familiarize

yourself with the package.

? After conversion, save the file by appending ‘_APA_style’ or ‘_IEEE_style’ to the

filename (e.g. ‘myfile_IEEE_style.docx’).

? We make the following simplifications

o In the reference list, you do not need to change the formatting of individual

references. Only make sure that there is numbering (for IEEE style) as

opposed to no numbering (for APA).

o For APA, the reference list should be sorted alphabetically. Example :

IEEE After conversion to APA

1. X. F. Li, The practice of life-insurance actuary,

Tianjin:NanKai University press, 2000.

2. S. H. Lu, "Information asymmetry and the Strategy of life

insurance underwriting", Insurance Studies, no. 9, pp. 39-

40, Sep. 2003.

3. X. A. Wang, "The underwriting of annuity insurance",

Insurance Studies, no. 3, pp. 45-46, Mar. 2004.

4. J. W. Han, M. Kamber, Data Mining: Concepts and

Techniques, San Francisco:Morgan Kaufmann Publishers,

2001.

J. W. Han, M. Kamber, Data Mining: Concepts and

Techniques, San Francisco:Morgan Kaufmann

Publishers, 2001.

X. F. Li, The practice of life-insurance actuary,

Tianjin:NanKai University press, 2000.

S. H. Lu, "Information asymmetry and the Strategy of

life insurance underwriting", Insurance Studies, no. 9,

pp. 39-40, Sep. 2003.

X. A. Wang, "The underwriting of annuity insurance",

Insurance Studies, no. 3, pp. 45-46, Mar. 2004.

o For IEEE, the reference list should be sorted numerically (smaller to greater),

where 1 refers to the first in-text citation in the paper, 2 refers to the next

citation, and so on. Example:

APA After conversion to IEEE

Cialdini, R. B. (2005). What's the best secret device for

engaging student interest? The answer is in the title. J. Soc.

Clin. Psychol. 24, 22–29. doi: 10.1521/jscp.24.1.22.59166

Vaughn, L., and Schick, T. (1999). How to Think About

Weird Things: Critical Thinking for a New Age. Mountain

View, CA: Mayfield Pub.

Willingham, D. T. (2008). Critical thinking: Why is it so

hard to teach? Arts Educ. Policy Rev. 109, 21–32.

[1] Willingham, D. T. (2008). Critical thinking: Why is

it so hard to teach? Arts Educ. Policy Rev. 109, 21–32.

[2] Cialdini, R. B. (2005). What's the best secret device

for engaging student interest? The answer is in the title.

J. Soc. Clin. Psychol. 24, 22–29. doi:

10.1521/jscp.24.1.22.59166

[3] Vaughn, L., and Schick, T. (1999). How to Think

About Weird Things: Critical Thinking for a New Age.

Mountain View, CA: Mayfield Pub.

o Your program should re-format all in-text citations.

? To implement your programme, you should only use basic Python including string

operations, as well as the docx module. Usage of Numpy, Pandas, the regular

expression module re, or any other modules not used in the first 4 lectures is not

permitted!

As a starting point, use Q3.py from Learning Central. Do not rename the file or the function.

Learning Outcomes Assessed

? Using the Python programming language to complete programming tasks

? Familiarity with basic programming concepts and data structures

? Reading and writing files

Criteria for assessment

Credit will be awarded against the following criteria; the coursework will allow students to

demonstrate their knowledge and practical skills and to apply the principles taught in

lectures. The functions you have implemented will be tested against different data sets. The

score each implemented function receives is judged by its functionality, efficiency, and/or

quality. The below tables explain the specific criteria for each question.

Criteria Distinction

(70-100%)

Merit

(60-69%)

Pass

(50-59%)

Fail

(0-50%)

Q1 Excellent working condition

with no errors

Mostly correct. Minor errors

in output

Major problem. Errors in

output

Mostly wrong or hardly

implemented

Criteria Distinction

(70-100%)

Merit

(60-69%)

Pass

(50-59%)

Fail

(0-50%)

Q2 Functionality

(70%)

fully working application that

demonstrates an excellent

understanding of the assignment

problem using relevant python

approach.

All required functionality is

met, and the application are

working probably with some

minors’ errors

Some of the

functionality

developed with and

incorrect output major

errors.

Faulty application with wrong

implementation and wrong

output

Quality (30%) Figures are elegant and show an

excellent understanding of

visualisation principles including

tick marks, labels, colouring, and

titles.

Figures show a good

understanding of visualisation

principles.

Figures show a basic

understanding of

visualisation

principles.

Missing figures.

Criteria Distinction

(70-100%)

Merit

(60-69%)

Pass

(50-59%)

Fail

(0-50%)

Q3 Functionality

(70%)

fully working application that

demonstrates an excellent

understanding of the assignment

problem using relevant python

approach.

All required functionality is

met, and the application are

working probably with some

minors’ errors

Some of the

functionality

developed with and

incorrect output major

errors.

Faulty application with wrong

implementation and wrong

output

Efficiency

(15%)

Excellent performance passing all

test cases

Good performance missed

some test cases

Passed some test cases

with incorrect output.

Did not pass any test case

Quality (15%) Excellent documentation with usage

of __docstring__ and comments

Good documentation with

minor missing of comments.

Fair documentation. No comments or documentation

at all


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