College

CUNY School of Professional Studies

Course-Name

Software Application Programming I

Course-Code

IS 210

In this assignment, we'll look at two practical examples of using lists to achieve a real-world objective.

The following tasks will either have you interacting with existing files in the assignment repository or creating new ones on the fly. Don't forget to add your interpreter directive, utf-8 encoding, and a short docstring with any new files that you create!

You've been tasked with organizing a party. There are a large number of families attending with each family to receive its own table. A table may only seat a set number of people, so families in excess of that number will need to be split across multiple tables but will not be combined with members of other families.

Create a function that can analyze arbitrary lists of families and return a total headcount for your caterer as well as a total number of tables needed.

1. Create a module named task_01.py
2. Create a function called get_party_stats() that takes two parameters:

   1. families, a list of families which are, themselves, lists of members, eg:

      [['Angel', 'Michael', 'Samuel'], ['Jennifer', 'James']]

   2. table_size, the maximum number of seats at each table, defaults to six (6)

3. Loop through your list with the for loop and count not only the total number of attendees but also the number of tables necessary to seat all the guests.

   Remember that, at this party, families are being anti-social and will not be seated with other families.

4. Return the results in a tuple:
   1. total number of guests
   2. total number of tables

-(-x // y)

>>> get_party_stats(['Jan'], ['Jen', 'Jess'], ['Jem', 'Jack', 'Janis']])
(6, 3)

>>> get_party_stats(['Jan'], ['Jen', 'Jess'], ['Jem', 'Jack', 'Janis']], 2)
(6, 4)

>>> import data
>>> data.get_party_list()

Imagine that you've been tasked with setting up appointments with a large client base. You have a system that capture the client name and the time of their appointments but you'd like to send a reminder e-mail blast to each client.

For our final exercise this week, we'll be preparing some data as though we were about to send automated e-mails. With lists and a basic for loop, there's little we can't accomplish!

1. Create a new module named task_02.py
2. Create a function named prepare_email() that takes one argument:

   1. appointments, A list of two-item tuples with the client's name and their appointment time as members:

      [('Wiley', 'Monday, March 16, 2015 05:16PM'), ...]

3. Use a for loop and .format() to create a new list with just the client's email body. The body of the email should use the following formatting string:

   'Dear {},\nI look forward to meeting with you on {}.\nBest,\nMe'

   Return your new list.

>>> prepare_email([('Jen', '2015'), ('Max', 'March 3')]
['Dear Jen,\nI look forward to meeting with you on 2015.\nBest,\nMe',
'Dear Max,\nI look forward to meeting you on March 3.\nBest\nMe']

Code must be functional and pass tests before it will be eligible for credit.

Lint tests check your code for syntactic or stylistic errors To execute lint tests against a specific file, simply open a terminal in the same directory as your code repository and type:

$ pylint filename.py

Where filename.py is the name of the file you wish to lint test.

Unit tests check that your code performs the tested objectives. Unit tests may be executed individually by opening a terminal in the same directory as your code repository and typing:

$ nosetests tests/name_of_test.py

Where name_of_test.py is the name of the testfile found in the tests directory of your source code.

All tests may be run simultaneously by executing the runtests.sh script from the root of your assignment repository. To execute all tests, open a terminal in the same directory as your code repository and type:

$ bash runtests.sh

Code should be submitted to GitHub by means of opening a pull request.

As-of Lesson 02, each student will have a branch named after his or her GitHub username. Pull requests should be made against the branch that matches your GitHub username. Pull requests made against other branches will be closed. This work flow mimics the steps you took to open a pull request against the pull branch in Week Two.

For a refresher on how to open a pull request, please see homework instructions in Lesson 01. It is recommended that you run PyLint locally after each file is edited in order to reduce the number of errors found in testing.

In order to receive full credit you must complete the assignment as-instructed and without any violations (reported in the build status). There will be automated tests for this assignment to provide early feedback on program code.

When you have completed this assignment, please post the link to your pull request in the body of the assignment on Blackboard in order to receive credit.