CSE 163

Section XX

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TA1 & TA2

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Question of the Day: If you were a data structure, which one would you be and why?

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Announcements

• Homework 1: Processing - due next Wednesday 4/16 @ 11:59pm
• Please use our course resources:
• Style Guide
• Citation Guide
• If you are stuck on a task for more than 20 minutes, please reach out!
• Office Hours
• Ed Discussion
• Resubmission information will be released soon

Recap

What we’ve Learned so far:

• Week 1
• Basic Python Syntax
• Control Flow
• Strings, Lists
• Week 2
• File Processing
• Built-in Data Structures
• List
• Set
• Dict
• Tuple

Game Plan

What We’ll Cover Today

• Review
• In-section group activity 1!

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After section, please fill out the groupmate evaluations for today through the Canvas assignment!

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List

• General purpose multi value storage
• Multi-type
• Mutable
• append()
• extend()
• remove(), pop()
• Indexable

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Use Cases:

• Need to store multiple values
• Unknown number of values

new_list = list()

new_list = []

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new_list.append("a") # ["a"]

new_list.append(2) # ["a", 2]

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new_list[0] # "a"

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new_list.pop(0) # returns “a”,

new_list is [2]

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Tuple

• General purpose multi value storage
• Multi-type
• Immutable
• Memory benefits
• Indexable
• Can be defined arbitrarily with parentheses or commas
• Can hold more than two values

Use Cases:

• Fixed size multi-value situations

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tup = ("hello", "world")

tup[0] # "hello"

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# takes in params a and b

def pack(a, b):

# returns a tuple of (a, b)

return a, b

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Sets

• Same-type unordered storage
• Can only store immutable values
• Does not store duplicates
• Lightning fast membership queries
• Mutable
• add(),remove(),update()
• union(), intersection(),

difference()

• Note: Cannot be initialized with {} Why?

Use Cases:

• Need fast membership queries
• Need to store only unique values

my_set = set()

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my_set.add(10) # {10}

my_set.add(30) # {10, 30}

my_set.add(30) # {10, 30}

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my_set.add([]) # Error!

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if 10 in my_set: # Membership Query

print("Found 10!")

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Dictionaries

• Stores unordered Key-Value pairs
• Keys must be immutable and unique
• Values can be mutable and duplicated
• Lots of tooling
• .keys()
• .values()
• .items()
• Use key-checking pattern

Use Cases:

• Storing corresponding mappings
• Student Name -> ID Number
• Region -> Area Code
• Very powerful.

d = dict()

d = {}

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d['Triangle'] = 3

d['Square'] = 4

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d['Square'] # 4

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items = ["cat", "hat", "bat", "cat"]

my_dict = dict()

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for i in items: # also really fast!

if i not in my_dict:

my_dict[i] = 0

my_dict[i] += 1

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my_dict

# {"cat": 2, "hat": 1, "bat": 1}

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File Processing

• “with open” syntax
• Read with .readlines() or .read() function
• Two approaches:
• Line based
• Token based

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file_path = "home/doggies.txt"

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with open(file_path) as f:

lines = f.readlines()

for l in lines:

print(l)

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file_path = "home/doggies.txt"

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with open(file_path) as f:

words = f.read().split()

for word in words:

print(word)

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