Week 5: More Functions

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Monday Wednesday Friday (No recording)


  • Lab 4 available now.

  • Quiz 2 on Friday 4 October at the start of class.

Week 5 Topics

  • Function/stack recap

  • While loop practice

  • The list data type

    • list operations

  • Functions with lists


Strings, Slicing, and Mutability (oh my!)

We know of several string features so far. Assuming s is a string variable:

  • len(s) to get the length of the string.

  • s + "more characters" to concatenate strings.

  • s * 3 to repeat a string multiple times.

  • Strings can be compared with relational operators (e.g., <, >, ==).

  • s[i] to index into the string and access the ith character.

String Slicing

Python supports a more powerful indexing (s[]) mechanism called slicing. Slicing is a very useful shorthand notation for grabbing characters from a string or items from a list. The syntax is similar to range() where you can have a start and a stop. Here are some examples:


>>> s[2:5]

>>> s[:6]

>>> s[6:]

The first example above (S[2:5]) grabs characters from positions 2, 3, and 4 (i.e., the start is 2, the stop is 5. Like range() string slicing goes up to but does not include the stop value.

The next two examples use implied defaults:

  • S[:6] is the same as S[0:6]

  • S[6:] is the same as S[6:len(s)] (grab from position 6 to the end).

Slicing exercise

Write a first_half() function in slicing.py that takes a string as input and returns the first half of the string. For odd-length strings, it’s ok to round down when determining "half".

You should test your function by writing a main() function that prompts the user for a string, passes that string to first_half(), saves the function’s return value into a variable, and then prints the result.

Advanced bonus exercise

Given a string representation of a strand of RNA, add a function to slicing.py that prints 3-character groups from the strand. For example, given this:


Can you print out this (i.e., the first three characters, then the next three, etc)?

 0 ACU
 3 AUG
 6 ACA
 9 UGC
12 CAA
15 CGC
18 UAG
21 CGU
24 CCU

Hint: the range() function can take three arguments: a start, a stop, and a step. The step determines how quickly range() counts up. For example:

# Counts from 0 to 25 by 5.
>>> for i in range(0, 26, 5):
...     print(i)

String mutability

While strings support indexing with brackets ([]), Python does not allow you to change the value in a position — the indices are read-only. For example:

>>> name = "Kevin"

# Normal indexing works fine for reading characters.
>>> name[1]

# Can't modify characters.
>>> name[1] = "a"
Traceback (most recent call last):
  File "", line 1, in 
TypeError: 'str' object does not support item assignment

Because strings allow reading but not writing, they’re said to be immutable. That is, their contents cannot be mutated (changed). Coming up soon, we’ll start to see data types that are mutable!

Writing functions

Recall the general template for writing a function:

Parameter description
Return value description (if any)
    # DO STUFF

    # Optional return statement to produce function output
    return VALUE

Modifying Parameters

What is the result of modifying the parameter variable for a function? Do the results effect the arguments in the original calling function, or are all changes ignored? We’ll explore this question in more depth this week.

Take a look at oops_squareFunc.py, which squares the parameter values in a function. What do you think will happen at the end of this program? Test it out and then we will trace through the program to see what happened.

Immutable data types (float,str,int,bool) can never be modified; thus, any changes to the variable require reassigning it a new value. Reassignment breaks any sharing of values between the parameter and its original argument.

Mutable data types are different - they can be modified without reassignment. As an example, we will return to the list datatype and see its methods.


Lists are a data type in Python that hold a collection of items. Lists are a type of sequence of items. As we will see, many of the operations we use on strings can also be used on lists, since both are sequences. There are two major differences between lists and strings: lists are a sequence of items where the items can be any of any type including (e.g., str, int, etc) while strings are strictly a sequence of characters. The second meaningful difference is that lists are mutable, meaning we can change the contents of the list. Strings are immutable.

List operations

We can do similar things to lists as with strings:

  • +: concatenation

  • len(lst): number of items in list

  • lst[i]: index into list to get ith item

  • lst[x:y]: slicing to retrieve items at indices [x,y) of the list

An operation that is unique to lists is append(), which adds an item to the list (and thus changes its content):

lst = []            # empty list
lst.append(5)       # add 5 to lst

lst2 = [0, 10, 20]
lst2.append(30)     # adds 30 to lst2

We can also iterate over lists just as we did with strings:

for i in range(len(lst)):    # we can iterate over a list
    print lst[i]

List exercise

Fill in the missing code in squareList-return.py to complete a program that squares the items of a list. In that file, main() prompts the user for input three times and builds a list out of the input values. Pass that list to squareList(), which should iterate over the list and produce a new list containing the squares of the original values.


List Mutability

Another thing we can do with lists that we can’t do with strings is change a single element:

text = "hello"
text[0] = "j"     # illegal: Strings are immutable.

lst = [0, 10, 20]
lst[0] = 5        # legal: Changes first element of list.

Side effects with mutable parameters

Previously, we showed that reassigning the parameter variable had no effect on the original argument that was sent to the function. This is always the case with immutable data types as once they are created, they cannot be modified.

However, there are circumstances where parameters of mutable data type (e.g., list) can be modified in a function, and the result will have the side effect of also changing the argument variable, despite not returning anything. This can be a desired outcome, or it could be the source of an error in what we want our program to do. Using stack traces to understand this phenomena will help us understand why.

in all circumstances, use of the assignment operator (=) resets any connections variables may share (the arrow gets changed to a new location); this applies to lists as well.

Exercise: list modification with side effects

In squareList-inplace.py, we will trace through the program and show that, despite not having a return value, our arguments are forever impacted by the call to the function.

Pseudorandom Numbers

Python has a random library that you can import into your programs and use to generate random numbers or choices. The actual numbers are pseudorandom, meaning they are not truly random. For CS 21 purposes though, they’re random enough.

random Library Synax

First import the random library:

from random import *

Then use one of the various functions in the library. The most useful functions are likely to be:

Function Description


Randomly choose and return one element from a sequence (e.g., one item from a list).

randrange(start, stop)

Chose and return a random number from [start, stop-1]. Like range(), the start is optional, and Python assumes 0 if you omit it.


Shuffle the contents of a list.


Returns a random float from [0,1).

random Examples

To simulate flipping a coin, you could use any of these:

flip = choice(["heads","tails"])
flip = choice("HT")
flip = randrange(2)                 # assume 0 is heads, 1 is tails
flip = random()                     # assume < 0.5 is heads

To simulate rolling a 6-sided die:

result = randrange(1,7)

To help decide what to order for dinner:

takeout = ["Pizza", "Wings", "Curry", "Sandwich", "Soup"]
dinner = choice(takeout)


Write a program, rps.py, to play rock-paper-scissors against a randomly-generated opponent’s choice. At the end, print a history of victories, defeats, and ties.

I would suggest dividing up the responsibilities of the game into three functions:

  • get_choice: Prompt the user for a choice and validate their input. Valid choices are "R" for rock, "P" for paper, "S" for scissors, and "QUIT" to exit the program. For any other input, you should prompt again. If the user types "QUIT", you can call the exit() function to stop the program.

  • simulate_game: Take the user’s choice as input, choose a random action for the opponent, and then compare to see who wins. Return one of "WIN", "LOSE", or "TIE".

  • main: Loop up to five times to simulate five rounds. In each round, call get_choice to get the user’s input and then call simulate_game to determine if the user won, lost, or tied. Print the result and also it in a results list, which you can print at the end of the five rounds to summarize the session.


On Friday, we took a quiz and worked on the RPS exercise above.