Nested Collections, Sorting and Data Modelling

Task 1: Multi-Key Nested Collection Sorter

Create a script named nested_sorter_task.py inside your lesson_26 folder:

• Define a list of employee dictionaries:

  staff = [
      {"name": "Alice", "dept": "ENG", "projects": 5, "rating": 4.8},
      {"name": "Bob", "dept": "HR", "projects": 2, "rating": 4.2},
      {"name": "Charlie", "dept": "ENG", "projects": 8, "rating": 4.8},
      {"name": "Diana", "dept": "HR", "projects": 4, "rating": 4.9}
  ]

• Import itemgetter from operator and sort staff primarily by dept (alphabetical A-Z) and secondarily by rating descending.
• Print the sorted output formatted cleanly using f-strings and tabular alignment padding.

Task 2: Deep Pattern Matching API Event Router

Create a script named nested_event_task.py:

• Define a function route_nested_payload(payload: dict).
• Use match-case deep structural pattern matching:
  • case {"header": {"type": "ORDER", "v": 1}, "body": {"items": [*items], "total": amt}} if amt >= 500.0 → Return VIP high-value order approval string.
  • case {"header": {"type": "ORDER", "v": 1}, "body": {"items": [*items], "total": amt}} → Return standard order processing string.
  • case {"header": {"type": "PING"}} → Return system heartbeat status string.
  • case _ → Return invalid payload error string.
• Test function with 3 dynamic payloads and print returned status strings.

Task 3: Master Review Capstone Project — Enterprise Organization Hierarchy OS (`org_hierarchy_os.py`)

Create a script named org_hierarchy_os.py inside your lesson_26 folder. This assignment tests and integrates **ALL concepts learned across Lessons 1 through 26**.

Project Architectural Requirements Specification:

1. Environment, Modules & Entry Guard (Lessons 18-20):
   • Import standard library modules (sys, datetime, json) and itemgetter from operator.
   • Wrap main application execution inside an if __name__ == "__main__": guard block.
2. Nested Collections & Data Modelling (Lesson 26):
   • Maintain a multi-level organization database dictionary: ORG_DATABASE = {} mapping company division IDs to nested department structures, employee record lists, and permission sets.
   • Implement safe traversal helper functions with Guard Clauses to extract deeply nested values without triggering runtime index or key exceptions.
3. Comprehensions & Core Data Structures (Lessons 21-25):
   • Use List and Dictionary Comprehensions to flatten, filter, and map nested division datasets dynamically.
   • Use set algebra (Union, Intersection, Difference) to evaluate cross-department employee permission overlaps.
4. Advanced Function Parameters & Scope (Lessons 15-17):
   • Structure code into pure modular functions with type hints, docstrings, early return Guard Clauses, and *args / **kwargs logging.
   • Incorporate sorted() with multi-key itemgetter and tuple lambda functions.
5. Indefinite & Definite Loops (Lessons 13-14):
   • Run the main application inside a continuous while True interactive CLI menu loop.
   • Iterate through output records using for loops with enumerate() and .items().
6. Pattern Matching CLI Dispatcher (Lesson 12):
   • Process user CLI command tokens inside a match-case block:
     • case ["ADD", "DEPT", div_id, dept_name, lead_name] → Create nested department entry.
     • case ["SEARCH", *keys_path] → Safely traverse nested structure and output extracted data.
     • case ["SORT", "STAFF", primary_key, secondary_key] → Perform dynamic multi-key sorting using itemgetter.
     • case ["AUDIT", div1_id, div2_id] → Perform set intersection on division security permissions.
     • case ["EXIT" | "QUIT"] → Terminate session safely using a sentinel flag.
     • case _ → Output command error message.
7. Conditionals, Formatting & Foundations (Lessons 1-11):
   • Sanitize all inputs using .strip() and .upper().
   • Format tabular reports using f-strings with field width alignment specifiers (:<15, :>10) and currency formatting (:,.2f).