The programming assignments for this course assume you use Python 3.11 (the version on the Gradescope Autograder). You may need to call python3 or python3.11 if python --version shows a Python version other than Python 3.11.x.
We recommend that you install Python to your local computer (see FAQs for setup).
But you are likely fine developing with Python 3.10 which is already installed on the Andrew Linux machines, and you should be able to call it with python3.
Whether you work on an instructional machine or your own, the project submission for grading will always be through Gradescope.
Project 0 will provide the following:
Feel free to read only the sections relevant to you. The programming assignment itself is Sections 6-9; all other exercises are just for practice.
Files to Edit and Submit: You will fill in portions of addition.py, buyLotsOfFruit.py, shopSmart.py and shopAroundTown.py in tutorial.zip during the assignment. You should submit these files with your code and comments. Please do not change the other files in this distribution or submit any of our original files other than these files.
Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's judgements -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.
Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, recitation, and Piazza are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask.
You may use the CMU Andrew Linux machines but we would recommend installing python to your personal computer. See our FAQs for setting up your personal machine. Note: If you plan on using your own local development environment, make sure that your Python version is correct and try not to use the Python that comes with data science packages like Anaconda.
Download tutorial.zip and use unzip to extract the contents of the zip file.
If you experience any trouble getting up and running with this tutorial, please come see us in office hours.
This is a barebones tutorial designed to familiarize you with the very basics of Python necessary to get through the course projects. If you would like more depth on any of these topics or more coverage of useful tools that are not as core to the language, check out the longer official Python tutorial.
You can download all of the files associated with the Python mini-tutorial as a zip archive: tutorial.zip.
The programming assignments in this course will be written in Python, an interpreted, object-oriented language that shares some features with both Java and Scheme. This tutorial will walk through the primary syntactic constructions in Python, using short examples.
We encourage you to type all Python code shown in the tutorial onto your own machine. Make sure it responds the same way.
You may find the Troubleshooting section helpful if you run into problems. It contains a list of the frequent problems previous students have encountered when following this tutorial.
Python can be run in one of two modes. It can either be used interactively, via an interpeter, or it can be called from the command line to execute a script. We will first use the Python interpreter interactively.
You invoke the interpreter by entering python3.11 at the Unix command prompt.
Note: depending on your setup, you may have to type python3 or just python if it already points to Python 3.11.
$ python3.11 --version
Python 3.11.9
To exit the interpreter, call exit().
The Python interpreter can be used to evaluate expressions, for example simple arithmetic expressions. If you enter such expressions at the prompt (>>>) they will be evaluated and the result will be returned on the next line.
>>> 1 + 1
2
>>> 2 * 3
6
Boolean operators also exist in Python to manipulate the primitive True and False values.
>>> 1==0
False
>>> not (1==0)
True
>>> (2==2) and (2==3)
False
>>> (2==2) or (2==3)
True
Like Java, Python has a built in string type. The + operator is overloaded to do string concatenation on string values.
>>> 'artificial' + "intelligence"
'artificialintelligence'
There are many built-in methods which allow you to manipulate strings.
>>> 'artificial'.upper()
'ARTIFICIAL'
>>> 'HELP'.lower()
'help'
>>> len('Help')
4
Notice that we can use either single quotes ' ' or double quotes " " to surround string. This allows for easy nesting of strings.
We can also store expressions into variables.
>>> s = 'hello world'
>>> print(s)
hello world
>>> s.upper()
'HELLO WORLD'
>>> len(s.upper())
11
>>> num = 8.0
>>> num += 2.5
>>> print(num)
10.5
In Python, you do not have declare variables before you assign to them.
Learn about the methods Python provides for strings. To see what methods Python provides for a datatype, use the dir and help commands:
>>> s = 'abc'
>>> dir(s)
['__add__', '__class__', '__contains__', '__delattr__', '__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__getslice__', '__gt__', '__hash__', '__init__','__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__','__repr__', '__rmod__', '__rmul__', '__setattr__', '__str__', 'capitalize', 'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs', 'find', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'replace', 'rfind','rindex', 'rjust', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']
>>> help(s.find)
Help on built-in function find:
find(...) S.find(sub [,start [,end]]) -> int Return the lowest index in S where substring sub is found, such that sub is contained within s[start,end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.
>> s.find('b')
1
Try out some of the string functions listed in dir (ignore those with underscores '_' around the method name). Press 'q' to back out of a help screen.
Python comes equipped with some useful built-in data structures, broadly similar to Java's collections package.
Lists store a sequence of mutable items:
>>> fruits = ['apple','orange','pear','banana']
>>> fruits[0]
'apple'
We can use the + operator to do list concatenation:
>>> otherFruits = ['kiwi','strawberry']
>>> fruits + otherFruits
>>> ['apple', 'orange', 'pear', 'banana', 'kiwi', 'strawberry']
Python also allows negative-indexing from the back of the list. For instance, fruits[-1] will access the last element 'banana':
>>> fruits[-2]
'pear'
>>> fruits.pop()
'banana'
>>> fruits
['apple', 'orange', 'pear']
>>> fruits.append('grapefruit')
>>> fruits
['apple', 'orange', 'pear', 'grapefruit']
>>> fruits[-1] = 'pineapple'
>>> fruits
['apple', 'orange', 'pear', 'pineapple']
We can also index multiple adjacent elements using the slice operator. For instance, fruits[1:3], returns a list containing the elements at position 1 and 2. In general fruits[start:stop] will get the elements in start, start+1, ..., stop-1. We can also do fruits[start:] which returns all elements starting from the start index. Also fruits[:end] will return all elements before the element at position end:
>>> fruits[0:2]
['apple', 'orange']
>>> fruits[:3]
['apple', 'orange', 'pear']
>>> fruits[2:]
['pear', 'pineapple']
>>> len(fruits)
4
The items stored in lists can be any Python data type. So for instance we can have lists of lists:
>>> lstOfLsts = [['a','b','c'],[1,2,3],['one','two','three']]
>>> lstOfLsts[1][2]
3
>>> lstOfLsts[0].pop()
'c'
>>> lstOfLsts
[['a', 'b'],[1, 2, 3],['one', 'two', 'three']]
Play with some of the list functions. You can find the methods you can call on an object via the dir and get information about them via the help command:
>>> dir(list)
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__',
'__delslice__', '__doc__', '__eq__', '__ge__', '__getattribute__',
'__getitem__', '__getslice__', '__gt__', '__hash__', '__iadd__', '__imul__',
'__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__',
'__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__',
'__rmul__', '__setattr__', '__setitem__', '__setslice__', '__str__',
'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse',
'sort']
>>> help(list.reverse) Help on built-in function reverse: reverse(...) L.reverse() -- reverse *IN PLACE* >>> lst = ['a','b','c']
>>> lst.reverse()
>>> ['c','b','a']
Note: Ignore functions with underscores "_" around the names; these are private helper methods. Press 'q' to back out of a help screen.
A data structure similar to the list is the tuple, which is like a list except that it is immutable once it is created (i.e. you cannot change its content once created). Note that tuples are surrounded with parentheses while lists have square brackets.
>>> pair = (3,5)
>>> pair[0]
3
>>> x,y = pair
>>> x
3
>>> y
5
>>> pair[1] = 6
TypeError: object does not support item assignment
The attempt to modify an immutable structure raised an exception. Exceptions indicate errors: index out of bounds errors, type errors, and so on will all report exceptions in this way.
A set is another data structure that serves as an unordered list with no duplicate items. Below, we show how to create a set, add things to the set, test if an item is in the set, and perform common set operations (difference, intersection, union):
>>> shapes = ['circle','square','triangle','circle']
>>> setOfShapes = set(shapes)
>>> setOfShapes
{'circle', 'square', 'triangle'}
>>> setOfShapes.add('polygon')
>>> setOfShapes
{'polygon', 'circle', 'square', 'triangle'}
>>> 'circle' in setOfShapes
True
>>> 'rhombus' in setOfShapes
False
>>> favoriteShapes = ['circle','triangle','hexagon']
>>> setOfFavoriteShapes = set(favoriteShapes)
>>> setOfShapes - setOfFavoriteShapes
{'polygon', 'square'}
>>> setOfShapes & setOfFavoriteShapes
{'circle', 'triangle'}
>>> setOfShapes | setOfFavoriteShapes
{'polygon', 'square', 'circle', 'hexagon', 'triangle'}
Note that the objects in the set are unordered; you cannot assume that their traversal or print order will be the same across machines!
The last built-in data structure is the dictionary which stores a map from one type of object (the key) to another (the value). The key must be an immutable type (string, number, or tuple). The value can be any Python data type.
Note: In the example below, the printed order of the keys returned by Python could be different than shown below. The reason is that unlike lists which have a fixed ordering, a dictionary is simply a hash table for which there is no fixed ordering of the keys (like HashMaps in Java). The order of the keys depends on how exactly the hashing algorithm maps keys to buckets, and will usually seem arbitrary. Your code should not rely on key ordering, and you should not be surprised if even a small modification to how your code uses a dictionary results in a new key ordering.
>>> studentIds = {'knuth': 42.0, 'turing': 56.0, 'nash': 92.0 }
>>> studentIds['turing']
56.0
>>> studentIds['nash'] = 'ninety-two'
>>> studentIds
{'knuth': 42.0, 'turing': 56.0, 'nash': 'ninety-two'}
>>> del studentIds['knuth']
>>> studentIds
{'turing': 56.0, 'nash': 'ninety-two'}
>>> studentIds['knuth'] = [42.0,'forty-two']
>>> studentIds
{'knuth': [42.0, 'forty-two'], 'turing': 56.0, 'nash': 'ninety-two'}
>>> studentIds.keys()
dict_keys(['turing', 'nash', 'knuth'])
>>> studentIds.values()
dict_values([56.0, 'ninety-two', [42.0, 'forty-two']])
>>> studentIds.items()
dict_items([('turing', 56.0), ('nash', 'ninety-two'), ('knuth', [42.0, 'forty-two'])])
>>> len(studentIds)
3
As with nested lists, you can also create dictionaries of dictionaries.
Use dir and help to learn about the functions you can call on dictionaries.
Now that you've got a handle on using Python interactively, let's look at a simple Python script that demonstrates Python's for loop. Open the file called foreach.py and you will see the following code:
# This is what a comment looks like
fruits = ['apples','oranges','pears','bananas']
for fruit in fruits:
print(fruit + ' for sale')
fruitPrices = {'apples': 2.00, 'oranges': 1.50, 'pears': 1.75}
for fruit, price in fruitPrices.items():
if price < 2.00:
print('{} cost {} a pound'.format(fruit, price))
else:
print(fruit + ' are too expensive!')
At the command line, use the following command in the directory containing foreach.py:
$ python3.11 foreach.py
apples for sale
oranges for sale
pears for sale
bananas for sale
oranges cost 1.500000 a pound
pears cost 1.750000 a pound
apples are too expensive!
Remember that the print statements listing the costs may be in a different order on your screen than in this tutorial; that's due to the fact that we're looping over dictionary keys, which are unordered. To learn more about control structures (e.g., if and else) in Python, check out the official Python tutorial section on this topic.
If you like functional programming you might also like map and filter:
>>> list(map(lambda x: x * x, [1,2,3]))
[1, 4, 9]
>>> list(filter(lambda x: x > 3, [1,2,3,4,5,4,3,2,1]))
[4, 5, 4]
You can learn more about lambda if you're interested.
The next snippet of code demonstrates Python's list comprehension construction:
nums = [1,2,3,4,5,6] oddNums = [x for x in nums if x % 2 == 1] print(oddNums) oddNumsPlusOne = [x+1 for x in nums if x % 2 ==1] print(oddNumsPlusOne)
This code is in a file called listcomp.py, which you can run:
$ python3.11 listcomp.py
[1,3,5]
[2,4,6]
Write a list comprehension which, from a list, generates a lowercased version of each string that has length greater than five.
Unlike many other languages, Python uses the indentation in the source code for interpretation. So for instance, for the following script:
if 0 == 1:
print('We are in a world of arithmetic pain')
print('Thank you for playing')
will output
Thank you for playing
But if we had written the script as
if 0 == 1:
print('We are in a world of arithmetic pain')
print('Thank you for playing')
there would be no output. The moral of the story: be careful how you indent! It's best to use four spaces for indentation -- that's what the course code uses.
Because Python uses indentation for code evaluation, it needs to keep track of the level of indentation across code blocks. This means that if your Python file switches from using tabs as indentation to spaces as indentation, the Python interpreter will not be able to resolve the ambiguity of the indentation level and throw an exception. Even though the code can be lined up visually in your text editor, Python "sees" a change in indentation and most likely will throw an exception (or rarely, produce unexpected behavior).
This most commonly happens when opening up a Python file that uses an indentation scheme that is opposite from what your text editor uses (aka, your text editor uses spaces and the file uses tabs). When you write new lines in a code block, there will be a mix of tabs and spaces, even though the whitespace is aligned. For a longer discussion on tabs vs spaces, see this discussion on StackOverflow.
As in Java, in Python you can define your own functions:
fruitPrices = {'apples':2.00, 'oranges': 1.50, 'pears': 1.75}
def buyFruit(fruit, numPounds):
if fruit not in fruitPrices:
print("Sorry we don't have {}".format(fruit))
else:
cost = fruitPrices[fruit] * numPounds
print("That'll be {} please".format(cost))
# Main Function
if __name__ == '__main__':
buyFruit('apples',2.4)
buyFruit('coconuts',2)
Rather than having a main function as in Java, the __name__ == '__main__' check is used to delimit expressions which are executed when the file is called as a script from the command line. The code after the main check is thus the same sort of code you would put in a main function in Java.
Save this script as fruit.py and run it:
$ python3.11 fruit.py
That'll be 4.800000 please
Sorry we don't have coconuts
Write a quickSort function in Python using list comprehensions. Use the first element as the pivot. You can find the solution in quickSort.py.
Although this isn't a class in object-oriented programming, you'll have to use some objects in the programming projects, and so it's worth covering the basics of objects in Python. An object encapsulates data and provides functions for interacting with that data.
Here's an example of defining a class named FruitShop:
class FruitShop:
def __init__(self, name, fruitPrices):
"""
name: Name of the fruit shop
fruitPrices: Dictionary with keys as fruit
strings and prices for values e.g.
{'apples':2.00, 'oranges': 1.50, 'pears': 1.75}
"""
self.fruitPrices = fruitPrices
self.name = name
print('Welcome to the {} fruit shop'.format(name))
def getCostPerPound(self, fruit):
"""
fruit: Fruit string
Returns cost of 'fruit', assuming 'fruit'
is in our inventory or None otherwise
"""
if fruit not in self.fruitPrices:
print("Sorry we don't have {}".format(fruit))
return None
return self.fruitPrices[fruit]
def getPriceOfOrder(self, orderList):
"""
orderList: List of (fruit, numPounds) tuples
Returns cost of orderList. If any of the fruit are
"""
totalCost = 0.0
for fruit, numPounds in orderList:
costPerPound = self.getCostPerPound(fruit)
if costPerPound != None:
totalCost += numPounds * costPerPound
return totalCost
def getName(self):
return self.name
The FruitShop class has some data, the name of the shop and the prices per pound of some fruit, and it provides functions, or methods, on this data. What advantage is there to wrapping this data in a class?
So how do we make an object and use it? Make sure you have the FruitShop implementation in shop.py. We then import the code from this file (making it accessible to other scripts) using import shop, since shop.py is the name of the file. Then, we can create FruitShop objects as follows:
import shop
shopName = 'Entropy'
fruitPrices = {'apples': 1.00, 'oranges': 1.50, 'pears': 1.75}
berkeleyShop = shop.FruitShop(shopName, fruitPrices)
applePrice = berkeleyShop.getCostPerPound('apples')
print(applePrice)
print('Apples cost ${:.2f} at {}.'.format(applePrice, shopName))
otherName = 'Enthalpy'
otherFruitPrices = {'kiwis':6.00, 'apples': 4.50, 'peaches': 8.75}
otherFruitShop = shop.FruitShop(otherName, otherFruitPrices)
otherPrice = otherFruitShop.getCostPerPound('apples')
print(otherPrice)
print('Apples cost ${:.2f} at {}.'.format(otherPrice, otherName))
print("My, that's expensive!")
This code is in shopTest.py; you can run it like this:
$ python3.11 shopTest.py Welcome to Entropy fruit shop 1.0 Apples cost $1.00 at Entropy. Welcome to Enthalpy fruit shop 4.5 Apples cost $4.50 at Enthalpy. My, that's expensive!
So what just happended? The import shop statement told Python to load all of the functions and classes in shop.py. The line berkeleyShop = shop.FruitShop(shopName, fruitPrices) constructs an instance of the FruitShop class defined in shop.py, by calling the __init__ function in that class. Note that we only passed two arguments in, while __init__ seems to take three arguments: (self, name, fruitPrices). The reason for this is that all methods in a class have self as the first argument. The self variable's value is automatically set to the object itself; when calling a method, you only supply the remaining arguments. The self variable contains all the data (name and fruitPrices) for the current specific instance (similar to this in Java). The print statements use the substitution operator (described in the Python docs if you're curious).
The following example illustrates how to use static and instance variables in Python.
Create the person_class.py containing the following code:
class Person:
population = 0
def __init__(self, myAge):
self.age = myAge
Person.population += 1
def get_population(self):
return Person.population
def get_age(self):
return self.age
We first compile the script:
$ python3.11 person_class.py
Now use the class as follows:
>>> import person_class
>>> p1 = person_class.Person(12)
>>> p1.get_population()
1
>>> p2 = person_class.Person(63)
>>> p1.get_population()
2
>>> p2.get_population()
2
>>> p1.get_age()
12
>>> p2.get_age()
63
In the code above, age is an instance variable and population is a static variable. population is shared by all instances of the Person class whereas each instance has its own age variable.
This tutorial has briefly touched on some major aspects of Python that will be relevant to the course. Here are some more useful tidbits:
range to generate a sequence of integers, useful for generating traditional indexed for loops:
for index in range(3):
print(lst[index])
reload command:
>>> reload(shop)
These are some problems (and their solutions) that new Python learners commonly encounter.
Problem:
ImportError: No module named py
Solution:
When using import, do not include the ".py" from the filename.
For example, you should say: import shop
NOT: import shop.py
Problem:
NameError: name 'MY VARIABLE' is not defined
Even after importing you may see this.
Solution:
To access a member of a module, you have to type MODULE NAME.MEMBER NAME, where MODULE NAME is the name of the .py file, and MEMBER NAME is the name of the variable (or function) you are trying to access.
Problem:
TypeError: 'dict' object is not callable
Solution:
Dictionary looks up are done using square brackets: [ and ]. NOT parenthesis: ( and ).
Problem:
ValueError: too many values to unpack
Solution:
Make sure the number of variables you are assigning in a for loop matches the number of elements in each item of the list. Similarly for working with tuples.
For example, if pair is a tuple of two elements (e.g. pair =('apple', 2.0)) then the following code would cause the "too many values to unpack error":
(a,b,c) = pair
Here is a problematic scenario involving a for loop:
pairList = [('apples', 2.00), ('oranges', 1.50), ('pears', 1.75)]
for fruit, price, color in pairList:
print('{} fruit costs {} and is the color {}'.format(fruit, price, color))
Problem:
AttributeError: 'list' object has no attribute 'length' (or something similar)
Solution:
Finding length of lists is done using len(NAME OF LIST).
Problem:
Changes to a file are not taking effect.
Solution:
reload(YOUR_MODULE) to guarantee your changes are being reflected. reload works similarly to import.All projects in this course will be autograded after you submit your code through Gradescope. For all projects you can submit as many times as you like until the deadline. Every project's release includes its autograder for you to run yourself. This is the recommended, and fastest, way to test your code, but keep in mind you need to submit in Gradescope to get your grade registered.
To get you familiarized with the autograder, we will ask you to code, test, and submit solutions for four questions. You will submit your files on the last tab in this sequence (scroll up and click on the right most tab).
You can download all of the files associated the autograder tutorial as a zip archive: tutorial.zip. Unzip this file and examine its contents:
$ unzip tutorial.zip $ cd tutorial $ ls VERSION addition.py autograder.py buyLotsOfFruit.py grading.py projectParams.py shop.py shopAroundTown.py shopSmart.py testClasses.py testParser.py test_cases textDisplay.py town.py tutorialTestClasses.py
This contains a number of files you'll edit or run:
addition.py: source file for question 1buyLotsOfFruit.py: source file for question 2shop.py: source file for question 3shopSmart.py: source file for question 3town.py: source file for question 4shopAroundTown.py: source file for question 4autograder.py: autograding script (see below)and others you can ignore:
test_cases: directory contains the test cases for each questiongrading.py: autograder codetestClasses.py: autograder codetutorialTestClasses.py: test classes for this particular projectprojectParams.py: project parametersThe command python3.11 autograder.py grades your solution to all four problems. If we run it before editing any files we get a page or two of output: