What is the time complexity of common operations on arrays, hash maps, and balanced trees?

Question presented to candidate: "You will be coding in Python. Before we start, tell me the time complexity of the basic operations — access, search, insert and delete — on an array, a hash map and a balanced binary search tree, and which Python types you would use for each."

What a strong answer should cover:

• Dynamic array: O(1) index access, O(1) amortized append, O(n) insert or delete at the front or middle (elements shift), O(n) search, O(log n) search if sorted.
• Hash map: O(1) average insert, lookup and delete; O(n) worst case under collisions; no ordering of keys.
• Balanced BST: O(log n) insert, delete and lookup because the height stays about log n; also min, max, floor, ceiling and ordered iteration; an unbalanced BST degrades to O(n).
• Choosing: hash map for pure lookups, tree for ordered or range queries, array for index access and fast iteration.
• Python mapping: list is a dynamic array, dict/set are hash tables, and there is no built-in balanced tree (bisect on a sorted list has O(n) inserts).

Code / implementation expected: Optional — use list, dict and bisect correctly and state the cost of each call.