THE DELIVERY MAN

 

The Delivery Man – Python List Operations 

Introduction

Lists in Python are used to store multiple items in a single variable. They are flexible, allowing us to add, remove, update, and manipulate elements easily. In this task, different list operations are performed to simulate a delivery man's workflow.

1. Creating a list and accessing an item

items = ["Notebook", "Pencil", "Eraser", "Ruler", "Marker"]
print(items[2])

Approach: Indexing
Why: Lists use zero-based indexing, so the third item is accessed using index 2.

2. Adding a new item

items.append("Glue Stick")
print(items)

Approach: append() method
Why: It adds an item at the end of the list, which is ideal for adding new deliveries.

3. Inserting an item

items.insert(2, "Highlighter")
print(items)

Approach: insert() method
Why: Used when we need to add an item at a specific position instead of the end.

4. Removing an item

items.remove("Ruler")
print(items)

Approach: remove() method
Why: Removes a specific value directly.
Alternative: pop() removes by index, but here we know the item name.

5. Printing a sublist

print(items[:3])

Approach: List slicing
Why: Efficient way to extract a portion of the list without modifying the original list.

6. Converting to uppercase

upper_items = [item.upper() for item in items]
print(upper_items)

Approach: List comprehension
Why: More concise and readable compared to using loops.

7. Checking if an item exists

if "Marker" in items:
    print("Marker is in the list")
else:
    print("Marker is not in the list")

Approach: Membership operator (in)
Why: Simple and efficient way to check existence.

8. Counting items

print(len(items))

Approach: len() function
Why: Gives total number of elements in the list.

9. Sorting the list

items.sort()
print(items)

Approach: sort() method
Why: Sorts the list in place, making it efficient.

10. Reversing the list

items.reverse()
print(items)

Approach: reverse() method
Why: Directly reverses the order of elements.

11. Nested list (item with time)

delivery = [["Notebook", "10AM"], ["Pencil", "11AM"]]
print(delivery[0])

Approach: Nested list
Why: Allows grouping related data together (item + time).

12. Counting occurrences

print(items.count("Ruler"))

Approach: count() method
Why: Counts how many times a value appears.

13. Finding index

print(items.index("Pencil"))

Approach: index() method
Why: Finds the position of an element in the list.

14. Extending the list

new_items = ["Pen", "Sharpener"]
items.extend(new_items)
print(items)

Approach: extend() method
Why: Adds multiple items at once.

15. Clearing the list

items.clear()
print(items)

Approach: clear() method
Why: Removes all elements efficiently.

16. Repeating items

items = ["Notebook"] * 3
print(items)

Approach: List multiplication
Why: Quick way to repeat elements.

17. Nested list comprehension

items = ["Notebook", "Pencil", "Eraser"]
result = [[item, len(item)] for item in items]
print(result)

Approach: Nested list comprehension
Why: Combines item and its length in a clean and compact way.

18. Filtering items

filtered = [item for item in items if "e" in item]
print(filtered)

Approach: Conditional list comprehension
Why: Efficient way to filter elements.

19. Removing duplicates

items = ["Notebook", "Pencil", "Notebook"]
unique_items = list(set(items))
print(unique_items)

Approach: Using set()
Why: Automatically removes duplicates.
Limitation: Order is not preserved.

Conclusion

This task demonstrates various list operations in Python such as adding, removing, sorting, and filtering elements. By using built-in methods and list comprehensions, we can write efficient and readable code. Choosing the right method depends on the situation, such as whether we need to maintain order or improve performance.