Version: 26sp

Data Structures

Lists

We've seen how to create lists by listing their elements:

my_nums: list[int] = [6, 7, 8, 9]
words: list[str] = ['never', 'gonna', 'give', 'you', 'up']

split() and join()

There's a built-in str function in Python that splits a str into separate words:

words: list[str] = 'never gonna give you up'.split()

results in ['never', 'gonna', 'give', 'you', 'up']

To split a str using something other than whitespaces, we can use the optional parameter sep:

words: list[str] = 'never gonna give you up'.split(sep = 'e')

results in ['n', 'v', 'r gonna giv', ' you up']

To do the opposite (combine a list of str into a single str), we use the join() function:

phrase: str = ' '.join(['never', 'gonna', 'give', 'you', 'up'])
also_phrase: str = 'e'.join(['n', 'v', 'r gonna giv', ' you up'])

The str to the left of the .join() is used as the "glue" or "fenceposts" between the strs when combining them. Both phrase and also_phrase are equal to 'never gonna give you up'

List indices

In Python (and most other programming languages), lists are indexed starting with 0 on the very left, and increasing as it goes to the right.

words: list[str] = 'never gonna give you up'.split()
second_word: str = words[1]
first_word: str = words[0]


first_three_words: list[str] = [first_word, second_word, words[2]]
print(first_three_words)   # ['never', 'gonna', 'give']

Remember: this means that the last index in the list is its length minus one.

Let's see what happens if we try to access an index that is larger than that.

IndexError: list index out of range

Unlike other programming langauges, Python also has a second set of indices starting with -1 on the very right, and counting down (more negative) as it steps leftward.

last_word: str = words[-1]
penultimate_word: str = words[-2]
print(f'{penultimate_word} {last_word}')      # you up

List slices

Python allows us to use "slicing" to get a sub-list (a contiguous part of the list).

letters: list[str] = list('abcdefghijklmnopqrstuvwxyz')
print(letters)  # ['a', 'b', 'c', 'd', ..., 'x', 'y', 'z']

second_third_fourth_letters: list[str] = letters[2:5]
print(second_third_fourth_letters)  # ['c', 'd', 'e']

To "slice" a list: in the square brackets, we provide the starting index (inclusive) and the stopping index (exclusive). The start must be to the left of the stop, and both must be valid indices.

It returns a new list that is a copy of that part of the original list, without modifying the original list.

If we want to start at the very beginning of the list, we can omit the starting index:

letters: list[str] = list('abcdefghijklmnopqrstuvwxyz')
print(letters[:4])  # ['a', 'b', 'c', 'd']

And if we want to end at the very end of the list, we can omit the stopping index:

letters: list[str] = list('abcdefghijklmnopqrstuvwxyz')
print(letters[20:])  # ['u', 'v', 'w', 'x', 'y', 'z']

Omitting both just creates a copy of the entire list:

letters: list[str] = list('abcdefghijklmnopqrstuvwxyz')
print(''.join(letters[:]))  # abcdefghijklmnopqrstuvwxyz

Poll: What is printed?

letters: list[str] = list('abcdefghijklmnopqrstuvwxyz')

print(f'{letters[-len(letters)]} {''.join(letters[23:])} {letters[-1]}')

a wxyz z
a xyz z
z wxyz z
z xyz z

Modifying a list

We can replace elements in a list:

my_nums[-1] = 900

We can also insert or append elements in a list (which makes the list longer):

my_nums: list[int] = [6, 7, 8, 9]

my_nums.insert(0, 5)
print(my_nums)  # [5, 6, 7, 8, 9]

my_nums.insert(3, 600)
print(my_nums)  # [5, 6, 7, 600, 8, 9]

my_nums.append(10)
print(my_nums)  # [5, 6, 7, 600, 8, 9, 10]

For insert(), the first argument is the index, and the second argument is the element to insert into the list.

We can append multiple elements at once using extend():

my_nums.extend([700, 800, 900])
print(my_nums)   # [5, 6, 7, 600, 8, 9, 10, 700, 800, 900]

2D lists

The elements in a list can be lists themselves. This is called a 2-dimensional list, or 2D list.

nums = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

first_row = nums[0]
print(first_row)  # [1, 2, 3]

last_element_of_first_row = first_row[-1]
print(last_element_of_first_row)  # 3

last_element_of_last_row = nums[-1][-1]
print(last_element_of_last_row)  # 9

Indexing and slicing work the same way in lists of any dimension.

Poll: What gets printed?

more_nums = [
    [col * row for col in range(row)]
    for row in range(6)
]

print(more_nums[4][:2])

[0, 2]
[0, 2, 4, 8]
[0, 4]
[0, 4, 8, 12]

List comprehension

Poll: Let's say we want to write a function that takes a list of integers, and returns a copy of it, but without the negative numbers. The way to write this function using the tools we already covered so far would be:

def positive_copy(nums: list[int]) -> list[int]:
    result: list[int] = list()
    for i in nums:
        if i >= 0:
            result.append(i)
    return result

def positive_copy(nums: list[int]) -> list[int]:
    result: list[int] = list()
    for i in range(len(nums)):
        if i >= 0:
            result.append(i)
    return result

def positive_copy(nums: list[int]) -> list[int]:
    result: list[int] = list()
    for i in range(len(nums)):
        if nums[i] >= 0:
            result.append(nums[i])
    return result

def positive_copy(nums: list[int]) -> list[int]:
    result: list[int] = list()
    for i in range(0, -len(nums), -1):
        if nums[i - 1] >= 0:
            result.insert(0, nums[i - 1])
    return result

Implementing that function by first creating an empty list, and then adding elements one by one, is not efficient. When we first create the empty list, Python doesn't know how long we expect the list to become, so it allocates a very small amount of space in the computer's memory. Then, as we add elements, it keeps having to make adjustments as it realizes that it didn't allocate enough space.

It's also hard to read, as we saw from that poll.

Instead, we can use list comprehension to let Python optimize it for efficiency.

We've seen some list comprehension already before today. Here's an example using list comprehension to give a copy of the original list, but with each element increased by one:

my_nums: list[int] = [6, 7, 8, 9]

increased_nums: list[int] = [i + 1 for i in my_nums] # list comprehension

print(increased_nums)  # [7, 8, 9, 10]

One way to look at this format is that we simply moved the body of a for loop to right before the for (after the opening bracket [).

If we want the resulting list to filter some elements, we add the if clause after the for clause.

Here is positive_copy() using list comprehension:

def positive_copy(nums: list[int]) -> list[int]:
    return [i for i in nums if i >= 0]

List comprehension can be used for things that aren't lists. Here is an example that iterates over a string and creates a set:

phrase: str = 'never gonna give you up'

letters: set[str] = {letter.lower() for letter in phrase}

print(letters)  # {'v', 'g', 'i', 'o', 'n', 'a', 'y', 'p', 'u', 'e', 'r', ' '}

List comprehension is a powerful tool. It can make for loops easier to read, though it is always up to you to decide which version is easiest to read for your code. Sometimes, a basic for loop is more readable.

Sets

A set is very similar to a list: it is a collection of items.

words: set[str] = {'hi', 'hi', 'hello', 'hi', 'howdy', 'hi'}

print(words)  # {'hi', 'hello', 'howdy'}

Differences between a set and a list:

A set is unordered
A set can only hold each item (at most) once -- no duplicates

Some set syntax

Creating a set:

words: set[str] = {'hi', 'hi', 'hello', 'hi', 'howdy', 'hi'}  # explicitly listing them

numbers: set[int] = set(range(5))  # using the set constructor
print(numbers)  # {0, 1, 2, 3, 4}

list_of_floats: list[float] = [3.4, 3.2, 2.9, 3.4, 3.0]
measurements: set[float] = set(list_of_floats)  # using the set constructor that takes an existing collection
print(measurements)  # {3.2, 3.0, 2.9, 3.4}

Adding and removing items, iterating over a set, and getting its size:

nums: set[float] = set()  # empty set

for i in range(100):
    random_float = round(random(), 2) # random float rounded to nearest hundredth
    nums.add(random_float)  # add it to the set

print(len(nums))  # print the size of the set

numbers: set[int] = set(range(5))
numbers.remove(3)
print(numbers)  # {0, 1, 2, 4}

Binary set operations:

Union (a | b): a set that has all elements that are in either set a or set b
Intersection (a & b): a set that has all elements that are in both set a and set b
Subset (a <= b): True if all elements in a are also in b, and False otherwise
- Strict subset (a < b): True if a <= b and a is not equal to b, and False otherwise
Subtraction (a - b): a set that has all elements in a that are not in b

nums_a: set[int] = set(range(1, 5))
nums_b: set[int] = set(range(3, 9))

print(nums_a | nums_b)  # {1, 2, 3, 4, 5, 6, 7, 8}
print(nums_a & nums_b)  # {3, 4}
print(nums_a <= nums_b) # False
print(nums_a - nums_b)  # {1, 2}

Poll: Why is there no binary "Addition" operation for sets? (There is Subtraction.)

Because it would be the same as the Intersection operation
Because it would be the same as the Union operation
Because it would be the same as the Subtraction operation
There is an Addition operation

Exercise: Let's write a function that takes a str and counts the number of unique (distinct) words in it.

def count_unique_words(text: str) -> int:
    return len(set(text.split()))

print(count_unique_words('hello hi hi hello howdy hi'))  # 3

Exercise: Let's write a function that checks if any two people in this room have the same birthday. It should have a loop that iterates (up to) 80 times. (Instructors should replace that number with the number of students in the room.) Each iteration, it should:

Ask the user to input their birthday via two separate ints: the month and the day (ask twice to get the two ints)
Store their birthday as a tuple
If that birthday is already in the set, return True
If not, add it to the set After the loop (which it should only reach if no two people have the same birthday), it should return False.

num_students: int = 80

def any_same_birthdays() -> bool:
    birthdays: set[Tuple[int, int]] = set()

    for _ in range(num_students):
        month: int = int(input('Please enter the month as a number between 1 and 12: '))
        day: int = int(input('Please enter the day as a number between 1 and 31: '))
        date: Tuple[int, int] = (month, day)
        if date in birthdays:
            return True
        else:
            birthdays.add(date)

    return False

Dictionaries

We use curly brackets ({ and }) to represent sets. But we also use them to represent another data type:

print(type({'hello'}))  # <class 'set'>
print(type({}))         # <class 'dict'>

Curly brackets, when empty (or non-empty, but formatted a specific way), denote a dictionary.

A dictionary is also known as an "associative array". It's like a list, but the indices are not required to be contiguous ints -- the indices can be of any type

A dictionary maps key --> value Each key can appear at most once (the keys are a set)

Here are two examples which map each animal (str) to their age (int):

ages: dict[str, int] = {'elephant': 12, 'cat': 10}
print(ages)  # {'elephant': 12, 'cat': 10}

also_ages: dict[str, int] = dict([('elephant', 12), ('cat', 10)])
print(also_ages)  # {'elephant': 12, 'cat': 10}  (same as before)

Some dictionary syntax

We can access a value given its key in two different ways: brackets ([key]) or using the get(key) method. The get(key) has the added benefit that it handles the case if the key is not in the dict.

ages: dict[str, int] = {'elephant': 12, 'cat': 10}

print(ages['cat'])  # 10
print(ages.get('cat'))  # 10
print(ages.get('dog'))  # None
print(ages.get('dog'), 3)  # 3
print(ages['dog'])  # raises KeyError

We can add or update a key -> value pair. If we add the same key twice, it overwrites the original value with the second value.

ages: dict[str, int] = {'cat': 10}

ages['elephant'] = 12
print(ages)  # {'cat': 10, 'elephant': 12}

ages.update([('elephant', 13)])
print(ages)  # {'cat': 10, 'elephant': 13}

ages['elephant'] = 14
print(ages)  # {'cat': 10, 'elephant': 14}

ages.update([('dog', 3)])
print(ages)  # {'cat': 10, 'elephant': 14, 'dog': 3}

We can iterate over a dict in two ways: over its keys, or over its key-value pairs:

ages: dict[str, int] = {'cat': 10, 'elephant': 14, 'dog': 3}

for key in ages:
    print(f"{key}'s age is {ages.get(key)}")

for key, value in ages.items():
    print(f"{key}'s age is {value}")

Exercise: Let's write a function that takes a str and returns a dictionary that maps from each unique word in the str to the number of times it appears.

def word_counter(text: str) -> dict[str, int]:
    word_counts: dict[str, int] = dict()
    for word in text.split():
        word_counts[word] = word_counts.get(word, 0) + 1
    return word_counts

print(word_counter('hello hi hi hello howdy hi'))  # {'hello': 2, 'hi': 3, 'howdy': 1}

Exercise: Let's write a function that helps us with Scrabble.

A very common situation: We are playing Scrabble. We see we have 3 'O's. What can we do?
The plan: get a map that gives us options based on a letter
Let's write a function that takes a letter as a parameter and returns a dictionary where:
- The keys are all possible frequencies of that letter (except zero)
- The values are the sets of words in the dictionary with that many of that letter
Here's a list of english words if you need one (the official Scrabble list is harder to get as a text file)

def scrabble_helper(letter: str) -> dict[int, set[str]]:
    result: dict[int, set[str]] = dict()
    with open('/path/to/dictionary.txt', 'r', encoding='utf-8') as english_dict:
        for word in english_dict.readlines():
            if letter in word:
                word = word.strip()
                letter_count = word.count(letter)
                if letter_count in result:
                    result[letter_count].add(word)
                else:
                    result[letter_count] = {word}
    return result

result: dict[int, set[str]] = scrabble_helper('r')

for key, value in result.items():
    if key > 2:
        print(f'{key}: {value}')

JSON

JSON (JavaScript Object Notation) is a popular format for storing data. It's very common for APIs to send us data in JSON format. Here is an example of one: https://openweathermap.org/api/one-call-3

JSON data is read as a dictionary. In this example below, we took the example API response from the Weather API and stored it in a file called example_json_data.json. (We removed the lines with ellipses (...), and the commas on the lines before them. We also added an ending bracket (}).) pprint (https://docs.python.org/3/library/pprint.html) is a library for printing data in a readable format.

import json, pprint

with open('example_json_data.json', 'r', encoding='utf-8') as f:
    data = json.load(f)
    pprint.pp(data)

Poll: Which data structure is best suited for this task: we're creating a product that works differently on different operating systems, and we want to know which operating systems we need to support

List
Tuple
Set
Dictionary

Poll: Which data structure is best suited for this task: storing the order in which young children should stand in line

List
Tuple
Set
Dictionary

Poll: Which data structure is best suited for this task: storing the 7 days of the week (Sunday, Monday, Tuesday, ..., Saturday)

List
Tuple
Set
Dictionary

Poll: Which data structure is best suited for this task: keeping track of each student's favorite color

List
Tuple
Set
Dictionary

(if time) map and filter

What if we want to perform an action for each element in a collection (like list comprehension), but we don't want to waste computer memory storing the resulting collection? The map() and filter() functions return an object that we can iterate over.

map(function, original_collection) returns an object that we can iterate over using a for loop, where each iteration uses the result of applying the provided function to the corresponding element in the original_collection.

phrase: str = 'never gonna give you up'

for word in map(str.upper, phrase.split()):
    print(word)

Prints each word of the phrase on its own line, in uppercase letters.

filter(function, original_collection) returns an object that we can iterate over using a for loop, but it only includes the elements of original_collection for which the function returns True.

def is_long(word: str) -> bool:
    return len(word) >= 4

phrase: str = 'never gonna give you up'

for word in filter(is_long, phrase.split()):
    print(word)

Prints each word of the phrase that is at least 4 characters long on its own line.

Poll: What does this function do?

def thing(n: int, m: int) -> float:
    total: int = sum([int(random() * n) for i in range(m)])
    return total / n

It returns a list of n random numbers between 0 and m
It returns a list of m random numbers between 0 and n
It returns the average of n random numbers between 0 and m
It returns the average of m random numbers between 0 and n

The Accumulator Pattern

A large part of this course will involve design patterns: a structure or template that software engineers have agreed solves a common software problem.

The Accumulator Pattern is used when we want to add up, or accumulate, a sequence of items.

Exercise: Let's write a function that:

Asks the user how many numbers they would like to input
Asks the user for that many numbers (floats)
Prints the minimum, maximum, and average of those numbers

Let's do it without creating any lists.

count = int(input('How many numbers? '))
sum: float = 0.0
min: float = float('inf')
max: float = float('-inf')
for _ in range(count):
    num = float(input('Please enter a number: '))
    sum += num
    if num < min:
        min = num
    if num > max:
        max = num
print(f'min: {min}\nmax: {max}\navg: {sum / count}')

Exercise for the reader: how can we use the Accumulator Pattern to also print the median of the numbers?

Poll: Which of these describes the Accumulator Pattern?

Initialize the loop variable before a loop over the sequence, and update the accumulator inside the loop
Initialize the loop variable before a loop over the sequence, and add (+) to it inside the loop
Initialize the accumulator variable before a loop over the sequence, and update it inside the loop
Initialize the accumulator variable to 0 before a loop over the sequence, and update it inside the loop

functools.reduce() and itertools.accumulate()

functools.reduce() and itertools.accumulate() are two functions that perform the Accumulator Pattern.

from functools import reduce

def add(num1: int, num2: int) -> int:
    return num1 + num2

my_nums: list[int] = [6, 7, 8, 9]

sum: int = reduce(add, my_nums)
print(sum) # 30

The function reduce(function, collection) takes two arguments: the function that adds (or otherwise "accumulates") elements of the collection, and the collection.

itertools.accumulate() works similarly, but instead of only returning the single result at the end, it returns an object that we can iterate over with all of the intermediate results, too. (It also swaps the order of the two arguments, so the collection is before the function.)

Poll: What does this do?

from itertools import accumulate

def max(num1: int, num2: int) -> int:
    if num1 > num2:
        return num1
    else:
        return num2

my_nums: list[int] = [7, 8, 2, 5, 1]

for num in accumulate(my_nums, max):
    print(num)

It iterates over my_nums, printing each number on its own line
It iterates over my_nums, printing the accumulated sum so far
It iterates over my_nums, printing the largest number so far
It iterates over my_nums, printing the same number over and over

Named and default parameters

Motivate by showing list.sort() documentation: https://docs.python.org/3/library/stdtypes.html#list.sort

Functions with lots of arguments:

def display_text(text: str, size: int, is_bold: bool, is_italic: bool, is_underlined: bool) -> None:
    ...

display_text('hello', 18, False, False, False)
display_text('goodbye', 18, True, False, False)

Pros: multiple options in the same function without compromising flexibility
Cons: error prone, must keep track of order of arguments, too many things to specify each time we call the function

Named arguments:

def display_text(text: str, size: int, is_bold: bool, is_italic: bool, is_underlined: bool) -> None:
    ...

display_text(text = 'hello', is_underlined = False, is_bold = False, is_italic = False, size = 18)

Make calls more readable
Enable you to reorder arguments

Default argument values:

def display_text(
    text: str, size: int = 18, is_bold: bool = False, is_italic: bool = False, is_underlined: bool = False
) -> None:
    ...

display_text(text = 'hello', is_bold = True)

If you usually pass the same value
Specify what the default value is for an argument that doesn't have a value when the function is called
In the function signature, arguments with default values must come after arguments without default values
If we have a function that is already widely used, and we want to add another parameter, give it a default value (so the existing code doesn't break, since they didn't specify a value for that parameter)

Note: Default argument values are evaluated when the function is declared, not when it is called. It is stuck with the value it got the first time, and does not "refresh" each time the function is called. Here's the example from our recommended textbook:

number = 5

def print_number(number: int = number) -> None:
    print(number)

number = 6 # This line does nothing; the default value for the argument is stuck at 5

print_number(8) # 8
print_number()  # 5
print(number)   # 6

Open-ended poll: What does this output? Why? (This is an example of code that could look like it's doing one thing, when it's actually doing something else)

def send_message_and_cc_self(message: str, sender: str, recipients: list[str] = []) -> None:
    recipients.append(sender) # add sender to recipients so they get a copy as well
    for r in recipients: # send message to each recipient
        print(f"Sending '{message}' from {sender} to {r}")

send_message_and_cc_self("note to self", "Rasika") # self-only message
send_message_and_cc_self("use RSA next time", "Eve", ["Alice", "Bob"]) # message to multiple people
send_message_and_cc_self("super secret", "admin") # another self-only message

Source: Tyler Yeats

Variable argument lists

Python allows us to have a function with an arbitrary number of arguments:

def print_args(*args: T) -> None:
    """Print each argument on a separate line"""
    for item in args:
        print(item)

print_args(1, 2, 3)

The function print_args() above can take any number of arguments, and they are of the generic type T. We can access them inside the function -- each argument to print_args() becomes an element in the tuple args. If there are no arguments, then args will be an empty tuple.

And, if we want a variable argument list, but with named arguments:

def print_args(**kwargs: T) -> None:
    """Print each argument on a separate line"""
    for argument_name, argument_value in kwargs.items():
        print(f'{argument_name}: {argument_value}')

print_args(a = 1, b = 2, c = 3)

a: 1
b: 2
c: 3

**kwargs stands for "keyword arguments", but you can name it anything you want. Notice that we use two asterisks for **kwargs and only one for *args.

Poll: How many arguments can I pass to this function?

(https://numpy.org/doc/stable/reference/generated/numpy.fromfunction.html)

Lists​

split() and join()​

List indices​

List slices​

Modifying a list​

2D lists​

List comprehension​

Sets​

Some set syntax​

Dictionaries​

Some dictionary syntax​

JSON​

(if time) map and filter​

The Accumulator Pattern​

functools.reduce() and itertools.accumulate()​

Named and default parameters​

Variable argument lists​