Final coding exercise on python

Question

Computer Information Systems

2020 – 2021

 Senior Final

Due 5/19/2021

100 points

Students will write a file processing program. Their will be two input files given to the student and they are to produce an output to print.

The input files are:  Track.txt and Genre.txt (Found in Canvas under Unit6 > Files)

open file information. When writing the open statement for both files add this to the statement(see in red)

open("Name of your file", "r", encoding="utf8")

The first line of each file contains the header information. The information will let you know what the name of the data elements are in your file. The data is separated by a pipe character “|”

Track.txt information:

This file contains information on music tracks, along with the size of the track, the length of the track, the genre of music(genre_id), composer, name of the track plus a few other fields.

Genre.txt_information:

This file contains information on the type of genre the music is plus a genre_id.

 

Processing:

You will read in both files in your program to process the relevant information. 

Program requirements are:

identify all rows that have Milliseconds greater or equal to 500,000 and Milliseconds less than or equal to 1,000,000 from the Track file.

You will also need to read in the Genre file to identify the Genre of music for each track that met the criteria in Milliseconds (see above).

Use the Genre_id from both files to match rows of data from the Track table with rows of data from the Genre table.

You will need to determine number of tracks for each genre_id from the above Milliseconds criteria.

You will need to calculate the total and average Milliseconds for each genre_id from the above Milliseconds criteria

You will need to calculate the total and average Bytes for each genre_id from the above Milliseconds criteria

 

Output:

The output report will be formatted in column format and consist of the following with headers.

Genre_id, Genre Name, Number of Tracks, Ttl Milliseconds, Avg Milliseconds, Ttl Bytes, Avg Bytes

Formatting round any calculations to 2 decimal places, output should contain at the most 2 decimal locations (no more or no less) for floats.

example given of format(not actual data results)

GenreID   GenreName   #Tracks    Ttl MS       Avg MS     Ttl Bytes    Avg. Bytes

1         Rock            4      2500000    625000.00    11450000   2862500.00

 

Submission:

Student will only submit a .py file for Grading.

There is a rubric please review it.

Any programs that don’t compile successfully (ie execute without errors, program runs through its logical conclusion) will be marked zero, regardless of what code is written. So even if your program has correct logic, make sure it runs error free when you turn it in.

Please keep comments lean and tight. There should be more lines of code than lines of comments.

Remove unnecessary print statements when submitting your file.

There is no makeup date or lateness for this Final.

Helpful Hints:

Read in the track.txt file and see what your working with before doing a full fledge program. Maybe extract the records that fit the criteria for milliseconds.

Identify the output fields (ie fields going on the report) and see what files contains them.

Look at using lists and the different methods you could use (append(), sort(), pop() are ones that may be useful)

Be familiar with split(), strip() methods.

Experiment trying different coding techniques and see what the results are. Remember there is no single way to solve the problem.

Answer 1

First, let's go through the steps you need to take.

1. **Read the files**: You'll need to use Python's built-in `open` function to read both `Track.txt` and `Genre.txt`. Note that these files use the pipe character `|` as a delimiter, and you're told that the first line of each file is a header line. You might want to use the `csv` module to read these files, specifying `|` as the delimiter and skipping the first line.

2. **Process the track data**: According to the prompt, you're interested in tracks with milliseconds greater than or equal to 500,000 and less than or equal to 1,000,000. You'll need to write code that checks each track's milliseconds and keeps track of the ones that fit these criteria.

3. **Match tracks with genres**: You'll also need to use the `genre_id` from both files to match the relevant tracks to their respective genres. A good way to do this would be to read all the genres into a dictionary first, using `genre_id` as the key. Then, when you're processing the tracks, you can easily find the genre for each track by looking up its `genre_id` in the dictionary.

4. **Calculate statistics for each genre**: For each genre that has tracks meeting the milliseconds criteria, you need to calculate the total and average milliseconds, and the total and average bytes. A good way to do this might be to create a dictionary for each genre, where each dictionary contains the total milliseconds, total bytes, and count of tracks. Then you can calculate the averages at the end.

5. **Output the report**: You'll need to print out the report in the specified format. If you've stored the data in the way I suggested, this should be fairly straightforward.

Here is a partial example to get you started:

```python

import csv

# Step 1: Read the files

with open('Genre.txt', 'r', encoding="utf8") as f:

reader = csv.reader(f, delimiter='|')

next(reader) # skip header

genres = {row[0]: row[1] for row in reader} # create a dictionary of genres

with open('Track.txt', 'r', encoding="utf8") as f:

reader = csv.reader(f, delimiter='|')

next(reader) # skip header

tracks = [row for row in reader if 500000 <= int(row[2]) <= 1000000] # filter tracks based on milliseconds

# Rest of the steps...

```

Remember to replace `'Genre.txt'` and `'Track.txt'` with the actual paths to your files.

This code reads both files and stores the genres in a dictionary and the tracks in a list. It also filters the tracks based on milliseconds while reading the file.

From here, you'll need to write the code to process the tracks, match them with their genres, calculate the necessary statistics, and print out the report. Good luck!