Due: 1/20 at 11:59 pm
The projects for this class assume you use Python 3.6.
Project 0 will cover the following:
Files to Edit and Submit: You will fill in portions of addition.py, buyLotsOfFruit.py, and shopSmart.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, section, and the discussion forum 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.
Discussion: Please be careful not to post spoilers.
Here are basic commands to navigate UNIX and edit files.
When you open a terminal window, you’re placed at a command prompt:
[ds442-ta@nova ~]$
The prompt shows your username, the host you are logged onto, and your current location in the directory structure (your path). The tilde character is shorthand for your home directory. Note your prompt may look slightly different. To make a directory, use the mkdir command. Use cd to change to that directory:
[ds442-ta@nova ~]$ mkdir foo
[ds442-ta@nova ~]$ cd foo
[ds442-ta@nova ~/foo]$
Use ls to see a listing of the contents of a directory, and touch to create an empty file:
[ds442-ta@nova ~/foo]$ ls
[ds442-ta@nova ~/foo]$ touch hello_world
[ds442-ta@nova ~/foo]$ ls
hello_world
[ds442-ta@nova ~/foo]$ cd ..
[ds442-ta@nova ~]$
Download python_basics.zip into your home directory (note: the zip file’s name may be slightly different when you download it). Use unzip to extract the contents of the zip file:
[ds442-ta@nova ~]$ ls *.zip
python_basics.zip
[ds442-ta@nova ~]$ unzip python_basics.zip
[ds442-ta@nova ~]$ cd python_basics
[ds442-ta@nova ~/python_basics]$ ls
foreach.py
helloWorld.py
listcomp.py
listcomp2.py
quickSort.py
shop.py
shopTest.py
Some other useful Unix commands:
cp copies a file or filesrm removes (deletes) a filemv moves a file (i.e., cut/paste instead of copy/paste)man displays documentation for a commandpwd prints your current pathxterm opens a new terminal windowfirefox opens a web browser& to a command to run it in the backgroundfg brings a program running in the background to the foregroundEmacs is a customizable text editor which has some nice features specifically tailored for programmers. However, you can use any other text editor that you may prefer (such as vi, pico, or joe on Unix; or Notepad on Windows; or TextWrangler on OS X; and many more).
To run Emacs, type emacs at a command prompt:
[ds442-ta@nova ~/python_basics]$ emacs helloWorld.py &
[1] 3262
Here we gave the argument helloWorld.py which will either open that file for editing if it exists, or create it otherwise. Emacs notices that this is a Python source file (because of the .py ending) and enters Python-mode, which is supposed to help you write code. When editing this file you may notice some of that text becomes automatically colored: this is syntax highlighting to help you distinguish items such as keywords, variables, strings, and comments. Pressing Enter, Tab, or Backspace may cause the cursor to jump to weird locations: this is because Python is very picky about indentation, and Emacs is predicting the proper tabbing that you should use.
Some basic Emacs editing commands (C- means “while holding the Ctrl-key”):
C-x C-s Save the current fileC-x C-f Open a file, or create a new file it if doesn’t existC-k Cut a line, add it to the clipboardC-y Paste the contents of the clipboardC-_ UndoC-g Abort a half-entered commandYou can also copy and paste using just the mouse. Using the left button, select a region of text to copy. Click the middle button to paste.
There are two ways you can use Emacs to develop Python code. The most straightforward way is to use it just as a text editor: create and edit Python files in Emacs; then run Python to test the code somewhere else, like in a terminal window. Alternatively, you can run Python inside Emacs: see the options under “Python” in the menubar, or type C-c ! to start a Python interpreter in a split screen. (Use C-x o to switch between the split screens, or just click if C-x doesn’t work).
If you want to spend some extra setup time becoming a power user, you can try an IDE like Eclipse (Download the Eclipse Classic package at the bottom). Check out PyDev for Python support in Eclipse.
Many of you will not have Python 3.6 already installed on your computers. Conda is an easy way to manage many different environments, each with its own Python versions and dependencies. This allows us to avoid conflicts between our preferred Python version and that of other classes. We’ll walk through how to set up and use a conda environment.
Prerequisite: Anaconda. Many of you will have it installed from classes such as EE 16A; if you don’t, install it through the link.
The command for creating a conda environment with Python 3.6 is:
conda create --name <env-name> python=3.6
For us, we decide to name our environment ds442, so we run the following command, and press y to confirm installing any missing packages.
[ds442-ta@nova ~/python_basics]$ conda create --name ds442 python=3.6
To enter the conda environment that we just created, do the following. Note that the Python version within the environment is 3.6, just what we want.
[ds442-ta@nova ~/python_basics]$ source activate ds442
(ds442) [ds442-ta@nova ~/python_basics]$ python -V
Python 3.6.6 :: Anaconda, Inc.
Note: the tag (<env-name>) shows you the name of the conda environment that is active. In our case, we have (ds442), as what we’d expect.
Leaving the environment is just as easy.
(ds442) [ds442-ta@nova ~/python_basics]$ source deactivate
[ds442-ta@nova ~/python_basics]$ python -V
Python 3.5.2 :: Anaconda custom (x86_64)
Our python version has now returned to whatever the system default is!
At the moment, students do not have the right permissions to download Python 3.6 or Conda on the lab machines. For P0, Python 3.5 (which is already installed) will suffice.
You can download all of the files associated with the Python mini-tutorial as a zip archive: python_basics.zip. If you did the unix tutorial in the previous tab, you’ve already downloaded and unzipped this file.
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 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 ds442 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 using the command python at the Unix command prompt; or if you are using Windows that doesn’t work for you in Git Bash, using python -i.
(ds442) [ds442-ta@nova ~]$ python
Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:07:29)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
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(...) method of builtins.str instance
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).
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:
>>> shapes = ['circle', 'square', 'triangle', 'circle']
>>> setOfShapes = set(shapes)
Another way of creating a set is shown below:
>>> setOfShapes = {‘circle’, ‘square’, ‘triangle’, ‘circle’}
Next, we show how to add things to the set, test if an item is in the set, and perform common set operations (difference, intersection, union):
>>> setOfShapes
set(['circle', 'square', 'triangle'])
>>> setOfShapes.add('polygon')
>>> setOfShapes
set(['circle', 'square', 'triangle', 'polygon'])
>>> 'circle' in setOfShapes
True
>>> 'rhombus' in setOfShapes
False
>>> favoriteShapes = ['circle', 'triangle', 'hexagon']
>>> setOfFavoriteShapes = set(favoriteShapes)
>>> setOfShapes - setOfFavoriteShapes
set(['square', 'polygon'])
>>> setOfShapes & setOfFavoriteShapes
set(['circle', 'triangle'])
>>> setOfShapes | setOfFavoriteShapes
set(['circle', 'square', 'triangle', 'polygon', 'hexagon'])
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()
['knuth', 'turing', 'nash']
>>> studentIds.values()
[[42.0, 'forty-two'], 56.0, 'ninety-two']
>>> studentIds.items()
[('knuth', [42.0, 'forty-two']), ('turing',56.0), ('nash', 'ninety-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 write a simple Python script that demonstrates Python’s for loop. Open the file called foreach.py, which should contain 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('%s cost %f a pound' % (fruit, price))
else:
print(fruit + ' are too expensive!')
At the command line, use the following command in the directory containing foreach.py:
[ds442-ta@nova ~/tutorial]$ python foreach.py
apples for sale
oranges for sale
pears for sale
bananas for sale
apples are too expensive!
oranges cost 1.500000 a pound
pears cost 1.750000 a pound
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]
The next snippet of code demonstrates Python’s list comprehension construction:
nums = [1, 2, 3, 4, 5, 6]
plusOneNums = [x + 1 for x in nums]
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:
[ds442-ta@nova ~]$ python 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. You can find the solution in listcomp2.py.
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 %s" % (fruit))
else:
cost = fruitPrices[fruit] * numPounds
print("That'll be %f please" % (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:
(ds442) [ds442-ta@nova ~]$ python 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 %s fruit shop' % (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:
return None
return self.fruitPrices[fruit]
def getPriceOfOrder(self, orderList):
"""
orderList: List of (fruit, numPounds) tuples
Returns cost of orderList, only including the values of
fruits that this fruit shop has.
"""
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 = 'the Berkeley Bowl'
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 %s.' % (applePrice, shopName))
otherName = 'the Stanford Mall'
otherFruitPrices = {'kiwis': 6.00, 'apples': 4.50, 'peaches': 8.75}
otherFruitShop = shop.FruitShop(otherName, otherFruitPrices)
otherPrice = otherFruitShop.getCostPerPound('apples')
print(otherPrice)
print('Apples cost $%.2f at %s.' % (otherPrice, otherName))
print("My, that's expensive!")
This code is in shopTest.py; you can run it like this:
[ds442-ta@nova ~]$ python shopTest.py
Welcome to the Berkeley Bowl fruit shop
1.0
Apples cost $1.00 at the Berkeley Bowl.
Welcome to the Stanford Mall fruit shop
4.5
Apples cost $4.50 at the Stanford Mall.
My, that's expensive!
So what just happended? The import shop statement told Python to load all of the functions and classes in shop.py. The line berkeleyShop = shop.FruitShop(shopName, fruitPrices) constructs an instance of the FruitShop class defined in shop.py, by calling the __init__ function in that class. Note that we only passed two arguments in, while __init__ seems to take three arguments: (self, name, fruitPrices). The reason for this is that all methods in a class have self as the first argument. The self variable’s value is automatically set to the object itself; when calling a method, you only supply the remaining arguments. The self variable contains all the data (name and fruitPrices) for the current specific instance (similar to this in Java). The print statements use the substitution operator (described in the Python docs if you’re curious).
The following example illustrates how to use static and instance variables in Python.
Create the person_class.py containing the following code:
class Person:
population = 0
def __init__(self, myAge):
self.age = myAge
Person.population += 1
def get_population(self):
return Person.population
def get_age(self):
return self.age
We first compile the script:
[ds442-ta@nova ~]$ python 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:
Use range to generate a sequence of integers, useful for generating traditional indexed for loops:
for index in range(3):
print(lst[index])
After importing a file, if you edit a source file, the changes will not be immediately propagated in the interpreter. For this, use the reload command:
>>> reload(shop)
These are some problems (and their solutions) that new Python learners commonly encounter.
Problem: ImportError: No module named py
Solution:
For import statements with import <package-name>, do not include the file extension (i.e. the .py string).
For example, you should use: 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('%s fruit costs %f and is the color %s' % (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.To get you familiarized with the autograder, we will ask you to code, test, and submit solutions for three questions.
You can download all of the files associated the autograder tutorial as a zip archive: tutorial.zip (note this is different from the zip file used in the UNIX and Python mini-tutorials, python_basics.zip). Unzip this file and examine its contents:
[ds442-ta@nova ~]$ unzip tutorial.zip
[ds442-ta@nova ~]$ cd tutorial
[ds442-ta@nova ~/tutorial]$ ls
addition.py
autograder.py
buyLotsOfFruit.py
grading.py
projectParams.py
shop.py
shopSmart.py
testClasses.py
testParser.py
test_cases
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 3autograder.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 python autograder.py grades your solution to all three problems. If we run it before editing any files we get a page or two of output:
[ds442-ta@nova ~/tutorial]$ python autograder.py
Starting on 1-21 at 23:39:51
Question q1
===========
*** FAIL: test_cases/q1/addition1.test
*** add(a, b) must return the sum of a and b
*** student result: "0"
*** correct result: "2"
*** FAIL: test_cases/q1/addition2.test
*** add(a, b) must return the sum of a and b
*** student result: "0"
*** correct result: "5"
*** FAIL: test_cases/q1/addition3.test
*** add(a, b) must return the sum of a and b
*** student result: "0"
*** correct result: "7.9"
*** Tests failed.
### Question q1: 0/1 ###
Question q2
===========
*** FAIL: test_cases/q2/food_price1.test
*** buyLotsOfFruit must compute the correct cost of the order
*** student result: "0.0"
*** correct result: "12.25"
*** FAIL: test_cases/q2/food_price2.test
*** buyLotsOfFruit must compute the correct cost of the order
*** student result: "0.0"
*** correct result: "14.75"
*** FAIL: test_cases/q2/food_price3.test
*** buyLotsOfFruit must compute the correct cost of the order
*** student result: "0.0"
*** correct result: "6.4375"
*** Tests failed.
### Question q2: 0/1 ###
Question q3
===========
Welcome to shop1 fruit shop
Welcome to shop2 fruit shop
*** FAIL: test_cases/q3/select_shop1.test
*** shopSmart(order, shops) must select the cheapest shop
*** student result: "None"
*** correct result: "<FruitShop: shop1>"
Welcome to shop1 fruit shop
Welcome to shop2 fruit shop
*** FAIL: test_cases/q3/select_shop2.test
*** shopSmart(order, shops) must select the cheapest shop
*** student result: "None"
*** correct result: "<FruitShop: shop2>"
Welcome to shop1 fruit shop
Welcome to shop2 fruit shop
Welcome to shop3 fruit shop
*** FAIL: test_cases/q3/select_shop3.test
*** shopSmart(order, shops) must select the cheapest shop
*** student result: "None"
*** correct result: "<FruitShop: shop3>"
*** Tests failed.
### Question q3: 0/1 ###
Finished at 23:39:51
Provisional grades
==================
Question q1: 0/1
Question q2: 0/1
Question q3: 0/1
------------------
Total: 0/3
Your grades are NOT yet registered. To register your grades, make sure
to follow your instructor's guidelines to receive credit on your project.
For each of the three questions, this shows the results of that question’s tests, the questions grade, and a final summary at the end. Because you haven’t yet solved the questions, all the tests fail. As you solve each question you may find some tests pass while other fail. When all tests pass for a question, you get full marks.
Looking at the results for question 1, you can see that it has failed three tests with the error message “add(a, b) must return the sum of a and b”. The answer your code gives is always 0, but the correct answer is different. We’ll fix that in the next tab.
Open addition.py and look at the definition of add:
def add(a, b):
"Return the sum of a and b"
"*** YOUR CODE HERE ***"
return 0
The tests called this with a and b set to different values, but the code always returned zero. Modify this definition to read:
def add(a, b):
"Return the sum of a and b"
print("Passed a = %s and b = %s, returning a + b = %s" % (a, b, a + b))
return a + b
Now rerun the autograder (omitting the results for questions 2 and 3):
[ds442-ta@nova ~/tutorial]$ python autograder.py -q q1
Starting on 1-21 at 23:52:05
Question q1
===========
Passed a = 1 and b = 1, returning a + b = 2
*** PASS: test_cases/q1/addition1.test
*** add(a, b) returns the sum of a and b
Passed a = 2 and b = 3, returning a + b=5
*** PASS: test_cases/q1/addition2.test
*** add(a, b) returns the sum of a and b
Passed a = 10 and b = -2.1, returning a + b = 7.9
*** PASS: test_cases/q1/addition3.test
*** add(a, b) returns the sum of a and b
### Question q1: 1/1 ###
Finished at 23:41:01
Provisional grades
==================
Question q1: 1/1
Question q2: 0/1
Question q3: 0/1
------------------
Total: 1/3
You now pass all tests, getting full marks for question 1. Notice the new lines “Passed a=…” which appear before “*** PASS: …”. These are produced by the print statement in add. You can use print statements like that to output information useful for debugging.
Add a buyLotsOfFruit(orderList) function to buyLotsOfFruit.py which takes a list of (fruit,pound) tuples and returns the cost of your list. If there is some fruit in the list which doesn’t appear in fruitPrices it should print an error message and return None. Please do not change the fruitPrices variable.
Run python autograder.py until question 2 passes all tests and you get full marks. Each test will confirm that buyLotsOfFruit(orderList) returns the correct answer given various possible inputs. For example, test_cases/q2/food_price1.test tests whether:
Cost of [('apples', 2.0), ('pears', 3.0), ('limes', 4.0)] is 12.25
Fill in the function shopSmart(orders,shops) in shopSmart.py, which takes an orderList (like the kind passed in to FruitShop.getPriceOfOrder) and a list of FruitShop and returns the FruitShop where your order costs the least amount in total. Don’t change the file name or variable names, please. Note that we will provide the shop.py implementation as a “support” file, so you don’t need to submit yours.
Run python autograder.py until question 3 passes all tests and you get full marks. Each test will confirm that shopSmart(orders,shops) returns the correct answer given various possible inputs. For example, with the following variable definitions:
orders1 = [('apples', 1.0), ('oranges', 3.0)]
orders2 = [('apples', 3.0)]
dir1 = {'apples': 2.0, 'oranges': 1.0}
shop1 = shop.FruitShop('shop1',dir1)
dir2 = {'apples': 1.0, 'oranges': 5.0}
shop2 = shop.FruitShop('shop2', dir2)
shops = [shop1, shop2]
test_cases/q3/select_shop1.test tests whether: shopSmart.shopSmart(orders1, shops) == shop1
and test_cases/q3/select_shop2.test tests whether: shopSmart.shopSmart(orders2, shops) == shop2
In order to submit your project, run python submission_autograder.py and submit the generated token file tutorial.token to the Project 0 assignment on Gradescope.