Lecture on 2D Collections in Python

Introduction to 2D Lists

• 2D Lists: A two-dimensional list is a list made up of lists.
  • Useful for grid or matrix data (e.g., Excel spreadsheet).
• Flexibility: 2D lists are more flexible compared to 2D tuples.

Creating 2D Lists

• Example Lists:
  • Fruits: [apple, orange, banana, coconut]
  • Vegetables: [celery, carrots, potatoes]
  • Meats: [chicken, fish, turkey]
• Creating a 2D List:
  • Combine individual lists into one outer list (e.g., groceries = [fruits, vegetables, meats]).

Accessing Elements in 2D Lists

• Print Entire 2D List: Lists are separated by commas within square brackets.
• Accessing Rows:
  • groceries[0] for fruits list.
  • groceries[1] for vegetables list.
  • groceries[2] for meats list.
• Accessing Elements within Rows:
  • Use two indices: groceries[row][column].
  • Example: groceries[0][0] gives apple.
  • Out of range index will cause an error.

Iterating Over 2D Lists

• Using Nested Loops:
  • Outer loop iterates over rows.
  • Nested loop iterates over elements within each row.
  • Adjust print statements for a grid-like structure by managing newline characters.

Other 2D Collections

• 2D Tuples: Tuples can be used for 2D collections.
  • Example: Numpad as a 2D tuple.
  • Tuples are ordered and unchangeable, hence faster than lists.
• 2D Sets: Possible but not suitable for ordered data as sets are unordered.

Exercise: Creating a 2D Keypad

• Data Type Selection:
  • Tuple preferred for ordered, unchangeable elements.
• Constructing a 2D Tuple for Numpad:
  • Four rows: (1,2,3), (4,5,6), (7,8,9), (*,0,#).
  • Use loops to print in grid format similar to a telephone keypad.*

Conclusion

• Summary: 2D collections (lists, tuples, or sets) are essential for handling grid or matrix data in Python.
• Flexibility and Performance: Choose between lists and tuples based on flexibility needs and performance considerations.