Assignment Overview - Python

An important question about the pandemic is the death rate which is often calculated as deaths per million of population. That rate is one metric for how well a country has responded to the pandemic. Once you have the ability to read files in Python you can examine raw data to answer such questions.

Program Specifications

In this project, you will read a file of data with total Covid deaths and the population of countries. You will then calculate the total deaths per million for countries. Assigning deaths to Covid is not consistently handled among countries and often not consistently within countries. However, there is more consistency with first-world countries so we will extract data for the G20 countries. (There isnt anything special about the G20 except that it makes the assignment more interesting.)

Since we are dealing with real data there will be complications. For example, there are columns of data that we arent interested in such as the deaths in the last seven days and there are rows of data that we arent interested in such as non-G20 countries. There is one header row that describe columns but they are not data so we need to ignore them. Also, some data is in the thousands with commas and Python cannot directly convert a number with commas into a Python number such as an int or float. You will be asked to write a function to handle commas.

Deaths-per-Million high-level algorithm

Your high-level algorithm will be:

1. Open

2. Open

3. Write

4. Loop

   a.

   b.

   c.

5. Close

an input file for reading a different file for writing

header information to the output file. through the input file, reading it line by line

Call a function to process each line i. Call a function to handle commas

Calculate deaths/million-population

If the data is for a G20 country, display it and write it to a file both files

6. Display which countries have deaths-per-million worse than the US

File Specification

The input file named data.txt has one header line and five fixed-field columns. Column 0 is a country name (in a 25-character field), columns 1, 2, and 3 are integers, and column 4 is a float. The numbers are in 10-character fields. A file such as data.txt with fixed-field columns allows you to use string slicing to extract individual values.

Your program must also meet the following specifications: 1. You must have and use at least these four functionsmore are fine. A proj05.py file with

function stubs is provided. a. defopen_file(ch)afile_pointer

1. If ch is 'w', open a file for writing; otherwise open a file for reading. Repeatedly prompt for a file name until a file is successfully opened. Use the try-except command. Usually, we would use except FileNotFoundError, but that works only for opening a file for reading so we want to use the more general file error for reading and writing so use

   except IOError. If ch is 'w', the prompt should be 'Enter a file name for writing: ' else the prompt should be 'Enter a file name for reading: '

2. Parameters: ch

3. Returns: file_pointer

4. Display: prompt and error message as appropriate

b. def handle_commas(s,T) a int or float or None

1. The parameters are s, a string, and T, a string. The expected values of T is the

   wordint or float; any other value returns None. If the value of T is int, the string s will be converted to an int and that int value will be returned. Similar for float. In both cases, you have to remove the commas from s before converting to int or float. If a value of s cannot be converted to an int or float, None will be returned (hint: use try-except)

2. Parameters: str, str

3. Returns: int or float or None

4. Display: nothing

c. def process_line(line) a str, int , float

1. The parameter is a string which is a line from the file. Extract the country

   (string in a 25-character field), deaths (int in a 10-character field), and population (float in a 10-character field from the END of the line). Strip leading and trailing spaces from the country name. Return the country, deaths, and population. Use the function handle_commas to process the numbers for deaths and population.

2. Parameters: str

3. Returns: str, int , float

4. Display: nothing

d. def main() Your main algorithm will be as follows calling the above functions.

1. Call open_file to open an input file for reading

2. Call open_file to open a different file for writing

3. Write header information (2 lines) to the output file and to the console.

   - The header information in the first line is: 'Country','Deaths','Population','Death Rate' - The header information in the second line is:

Deliverables

'','','Millions','per Million' For both lines, use the following formatting for the headers (also provided in the strings.txt file): "{:<20s}{:>10s}{:>14s}{:>14s}"

iv. Loop through the input file, reading it line by line 1. Call a function to process each line

a. Call a function to handle commas

2. Calculate deaths/million-population

3. If the data is for a G20 country, display it and write it to the output file.

   use the following string formatting for the numbers (also provided in the strings.txt file): "{:<20s}{:>10,d}{:>14,.2f}{:>14,.2f}"

5. Close both files

6. Display which countries have deaths-per-million worse than the US

   To determine which countries are worse than the US we provide the deaths-per- million for the US (1277.10). Note that the US rate is rounded to 2 digits after the decimal point. Start with an empty string before the loop and build a comma- separated string of the countries that you will display after the loop. Note that this item is not written to the output file. It is only written to the console.

The deliverable for this assignment is the following file: proj05.py -- your source code solution

Be sure to use the specified file name and to submit it for grading via Mimir before the project deadline.

Notes and Hints:

1. To clarify the project specifications, sample output is appended to the end of this document.

2. Items 1-9 of the Coding Standard will be enforced for this projectnote the change to include

   more items.

3. You can test functions separatelythat can be a huge advantage in developing correct code

   faster! If you cannot figure out how to do that, ask your TA for guidance.

4. We provide a string with the names of G20 countries. Hint use the string operator in to see if

   a country is in the G20.

    G20 = "Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, South Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, United Kingdom, USA, European Union" 

5. We also provide a constant for the US death rate per Millions. Hint use this constant to look for the countries that have death rates high than the US. Note that the constant is rounded to 2 digits after the decimal point. US_RATE = 1277.10

6. Do not hard code your solutionsthe result is a zero. Hard coding means that your program for the specific tests rather than having a generic solution.

Sample Interaction:

Function Test handle_commas()

s,T: 5 int Instructor: 5 Student : 5 -------------------- s,T: 5.3 float Instructor: 5.3 Student : 5.3 -------------------- s,T: 1,234 int Instructor: 1234 Student : 1234 -------------------- s,T: 1,234.56 float Instructor: 1234.56 Student : 1234.56 -------------------- s,T: 5.3 xxx Instructor: None Student : None -------------------- s,T: aaa int Instructor: None Student : None -------------------- s,T: 1,234.56 int Instructor: None Student : None 

Function Test process_line()

line: Tunisia Instructor: Tunisia 3596 Student : Tunisia 3596 -------------------- line: China Instructor: China 4746 1397.72 Student : China 4746 1397.72 -------------------- line: India Instructor: India 140958 1366.42 Student : India 140958 1366.42

 11.69 0 1,397.72 385 1,366.42 

11.69 11.69

3,596 336 35

 4,746 3 140,958 2,836 

Test Case 1

Enter a file name for reading: data.txt Enter a file name for writing: outfile.txt 

Country

United Kingdom Italy USA Mexico 

France Argentina Brazil South Africa Germany Canada Russia Turkey 

Saudi Arabia India Indonesia Japan Australia South Korea China 

Deaths

98,339

 85,881 419,196 150,273 

72,590

 47,034 217,664 41,117 53,127 18,868 68,841 25,210 6,355 153,587 28,132 5,193 909 1,371 4,807 

Population Millions 66.83 60.30 328.24 127.58 67.06 44.94 211.05 58.56 83.13 37.59 144.37 83.43 34.27 1,366.42 270.63 126.26 25.36 51.71 1,397.72 

 Death Rate per Million 1,471.48 1,424.23 1,277.10 1,177.87 1,082.46 1,046.60 1,031.34 702.13 639.08 501.94 476.84 302.17 185.44 112.40 103.95 41.13 35.84 26.51 3.44 

Countries with higher death rates than USA per million. United Kingdom, Italy 

Test Case 2

Enter a file name for reading: xxx Error opening file. Enter a file name for reading: yyyy.txt Error opening file. 

Enter a file name for reading: data.txt Enter a file name for writing: xxx/xxx Error opening file. Enter a file name for writing: outfile.txt 

Country Deaths 

United Kingdom 98,339 Italy 85,881 USA 419,196 Mexico 150,273 France 72,590 Argentina 47,034 Brazil 217,664 South Africa 41,117 Germany 53,127 Canada 18,868 Russia 68,841 

Population Millions 66.83 60.30 328.24 127.58 67.06 44.94 211.05 58.56 83.13 37.59 144.37 

 Death Rate per Million 1,471.48 1,424.23 1,277.10 1,177.87 1,082.46 1,046.60 1,031.34 702.13 639.08 501.94 476.84 

Turkey 25,210 Saudi Arabia 6,355 India 153,587 Indonesia 28,132 Japan 5,193 Australia 909 South Korea 1,371 China 4,807 

83.43

 34.27 1,366.42 270.63 126.26 25.36 51.71 1,397.72 

302.17 185.44 112.40 103.95 

 41.13 35.84 26.51 

3.44

Countries with higher death rates than USA per million. United Kingdom, Italy 

Scoring Rubric

Computer Project #05 Scoring Summary 

General Requirements 

______ 5 pts Coding Standard 1-9 (descriptive comments, function header, etc...)

Implementation: 

__0__ (7 pts) open_file (manual grading) __0__ (7 pts) Function Test handle_commas __0__ (7 pts) Function Test process_line __0__ (5 pts) Test 1 

__0__ (5 pts) Test 2 

__0__ (4 pts) Test 3 (same as Test 1 but checking if output correct) 

file is