Working with Lists

Beginner Python
listappendpopsortslicecopylist comprehension

Objective

Master list creation, indexing, slicing, mutation, and common list operations.

Tools & Technologies

  • list
  • append
  • pop
  • sort
  • slice
  • copy
  • list comprehension

Key Commands

lst.append(x)
lst.sort(key=lambda x: x[1])
lst[1:4]
lst.pop()
[x for x in lst if x > 0]
sorted(lst, reverse=True)

Architecture Overview

graph TD LST[list = [10, 20, 30, 40, 50]] LST --> IDX[Indexing\nlst[0]=10 lst[-1]=50] LST --> SLC[Slicing\nlst[1:3]=[20,30]] LST --> MUT[Mutation\nlst.append(60)\nlst.pop()] LST --> SORT[Sorting\nsorted(lst)\nlst.sort()] LST --> COMP[Comprehension\n[x*2 for x in lst]] style LST fill:#1a1a2e,stroke:#00d4ff,color:#e0e0e0

Step-by-Step Process

01
Indexing and Slicing

Access individual elements and ranges.

lst = [10, 20, 30, 40, 50]
lst[0]     # 10 (first)
lst[-1]    # 50 (last)
lst[1:3]   # [20, 30] (index 1 up to but not 3)
lst[:3]    # [10, 20, 30]
lst[2:]    # [30, 40, 50]
lst[::2]   # [10, 30, 50] (every 2nd)
lst[::-1]  # [50, 40, 30, 20, 10] (reversed)
02
Mutating Lists

Add, remove, and modify elements.

lst.append(60)        # add to end
lst.insert(2, 25)     # insert at index 2
lst.extend([70, 80])  # add multiple
lst.pop()             # remove and return last
lst.pop(0)            # remove and return index 0
lst.remove(30)        # remove first occurrence of 30
del lst[2]            # delete index 2
lst.clear()           # empty the list
03
Sorting and Searching

Sort and search lists efficiently.

nums = [3, 1, 4, 1, 5, 9, 2]
nums.sort()                # in-place
nums.sort(reverse=True)    # descending
sorted(nums)               # returns new list

# Sort by key
people = [('Alice', 25), ('Bob', 22)]
people.sort(key=lambda x: x[1])  # by age

# Search
30 in lst              # True/False
lst.index(30)          # first position
lst.count(30)          # occurrences

Challenges & Solutions

  • list.sort() modifies in place, sorted() returns new list — common confusion
  • Shallow copy with lst.copy() — nested objects still share references

Key Takeaways

  • List comprehensions are typically 30-50% faster than equivalent for loops
  • Use collections.deque for efficient O(1) append/pop from both ends