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Comments from last section

• Real world examples of trees in Python
  • Binary Search Trees (you'll see this in CS 61B)
  • Makes searching for ordered items very easy and fast
• Your take on AI being really good at mimicking peoples voices.
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Temperature Check

• Mutability
• Iterators
• Generators (today's lecture, I think)

List Mutation Functions (adding)

• .append(element)
  • Adds elements to the end of the list
  • All elements go in one new box (can get nested lists if the element passed in is a list)
• .extend(iterable)
  • Concatenates two lists together (typcially iterable is a list)
• .insert(index, element)
  • Inserts element at index
  • Does not replace elements - this operation instead makes the list longer.
• All these functions return None once you use them

List Mutation Functions (removing)

• .remove(element)
  • Removes first appearance of element in list
  • Errors if it's unable to remove an element
• .pop(optional index)
  • Removes and returns element at the given index
  • If index is not provided, it defaults to the last element in the list.

Mutating Lists

• List mutation functions can modify an existing list
• Slicing will create a new list
  • Examples later
• a = a + b will create a new list
• a += b does not create a new list
• Indexing into a list and changing the element at that list will mutate the list:
  • a[0] = 7 will change the first element in a to be 7.

Identity vs Equality

• is will check whether 2 objects are the same thing (i.e. pointing to the same object)
• == will check if two objects have the same value

Identity vs Equality

• is will check whether 2 objects are the same thing (i.e. pointing to the same object)
• == will check if two objects have the same value

Mutating Lists (Example)

(For those reading the slides later, put this into tutor.cs61a.org)

Shallow Copy vs Deep Copy

• Shallow Copy
  • Only copies the first layer of a list
  • If we have a nested list, we only copy the arrow (not the list itself)
  • Create a new list where you copy over whatever is in the same box
• Deep Copy
  • Makes a complete copy of everything in a list
  • Very slow operation - no easy way to do this
• Python uses shallow copies (as do most languages) when copying lists!

Example: Shallow Copy vs Deep Copy

(For those reading the slides later, put this into tutor.cs61a.org)

Iterators

• Iterable
  • Old View: Something we can go through one at a time
  • New View: Something we can call iter on - this will return an iterator.
• Iterator
  • A type of object that lets us keep track of which element is next in the sequence

Functions to Interact with Iterators

• iter(iterable)
  • Takes in an iterable and returns an iterator.
    • Calling iter on an iterable makes a brand new iterator
    • Calling iter on an iterator returns that same iterator (not a copy!) and does not change the state of that iterator
• next(iterator)
  • Takes in an iterator and outputs the next element
    • Raises StopIteration when it has no more elements to go through
    • Cannot call next on an iterable

Analogy

• Iterable = Book
• Iterator = Bookmark
• Calling iter on an iterable (book) will create a new bookmark
• Calling iter on an iterator (bookmark) will just give you the same bookmark (no reason to mark where a bookmark is)
• Calling next on an iterator moves the bookmark to the next chapter

do some terminal example pls thx

Generators

• Generators are more specific versions of iterators that you can create yourself!
• When a generator function is called, it returns a generator object!
• The body of a generator function is not evaluated until next is called on the returned generator object.
• Generator functions look like normal functions, but use yield instead of return. Python will automatically see yield and determine a function a generator function if necessary.

Generators (continued)

• When next is called on a generator object, the body of a function is executed until yield is reached.
• From there, the yield statement will return a value, and then pauses that specific function at that moment (by saving the frame, and the line that it's on)
• When next is called again, we continue where we left off, until yield is reached again.
• StopIteration will be raised at the end of a generator function

Example

yield from

• You can use yield from <iterable> if you want to yield every value from an iterable. These are equivalent:

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