After finishing this lesson, you will be able to confidently do the following 3 things.

• ▸✅ List slicing + comprehension
• ▸✅ Choosing list vs tuple + deep copy (copy.deepcopy)
• ▸✅ sort vs sorted (whether the original is modified)

Keep the learning objectives as a checklist, and close the lesson once you can answer all of them.

list = a data structure that is ordered + mutable + can hold any type. The most frequently used data structure in Python.

1. Creating a list

numbers = [1, 2, 3, 4, 5]
names = ["Gildong Hong", "Mongryong Lee", "Chunhyang Seong"]
mixed = [1, "two", 3.0, True]      # Mixing types is OK
empty_list = []

print(len(numbers))     # 5 (count)
print(type(numbers))    # <class 'list'>

2. Indexing — starts at 0

fruits = ["apple", "pear", "persimmon", "tangerine"]

print(fruits[0])       # "apple"  (first)
print(fruits[-1])      # "tangerine"   (last) ← Negative numbers OK
print(fruits[-2])      # "persimmon"   (second from last)

⚠️ fruits[10] → IndexError: list index out of range

3. Slicing — [start:end]

numbers = [10, 20, 30, 40, 50]

print(numbers[1:4])     # [20, 30, 40]   ← From 1 up to just before 4
print(numbers[:3])      # [10, 20, 30]   ← From start up to just before 3
print(numbers[2:])      # [30, 40, 50]   ← From 2 to the end
print(numbers[::-1])    # [50, 40, 30, 20, 10]   ← Reversed

Key point: The end index is exclusive (not included). [::-1] is the idiom for reversing a list.

4. Adding and removing

fruits = ["apple", "pear"]

fruits.append("persimmon")       # ["apple", "pear", "persimmon"]
fruits.insert(1, "grape")  # ["apple", "grape", "pear", "persimmon"]
fruits.remove("pear")       # ["apple", "grape", "persimmon"]
last_fruit = fruits.pop()    # last_fruit = "persimmon", list = ["apple", "grape"]

print(fruits)

5. Searching and checking

fruits = ["apple", "pear", "persimmon"]

print("pear" in fruits)         # True
print("grape" in fruits)       # False
print(fruits.index("persimmon"))     # 2 (which index?)
print(fruits.count("apple"))   # 1 (how many?)

6. Sorting

numbers = [3, 1, 4, 1, 5, 9, 2]

numbers.sort()                # Modifies original
print(numbers)                # [1, 1, 2, 3, 4, 5, 9]

original = [3, 1, 4]
sorted_list = sorted(original)         # Returns a new list
print(original, sorted_list)          # [3, 1, 4] [1, 3, 4]

numbers.sort(reverse=True)
print(numbers)                # [9, 5, 4, 3, 2, 1, 1]

sort() = modifies the original / sorted(x) = returns a new list.

7. Iteration (for)

```python
fruits = ["apple", "pear", "persimmon"]

for fruit in fruits:
print(f

1. Negative index: -1 refers to the last element
2. Slicing: [start:end:step]
3. sort() modifies the original; sorted() returns a new list

Python basic data structures: a list is an ordered mutable collection, a tuple is immutable, a dictionary holds key-value pairs, and a set holds unique values. Use list comprehension [x for x in lst if cond] for concise creation. Use dict.get(key, default) to safely retrieve values. The collections module provides: Counter, defaultdict, deque, OrderedDict.

Knowing the concepts in this lesson lets you give AI specific instructions. Instead of a vague 'fix this,' you make vocabulary-driven requests — that is the starting point for saving tokens.

• ▸'Convert this for + append into a list comprehension'
• ▸'Check whether deep copy (copy.deepcopy) is needed in this code'

Why this saves tokens

Without knowing the concepts, even after receiving an AI answer you have to ask 'What does that mean?' again. That follow-up question is what eats your tokens. Learn the concept once, and the conversation ends in one round.