#341

Medium

auto_awesomeBinary-tree traversal and state tracking

LeetCode Problem Workspace

Flatten Nested List Iterator

Implement an iterator to flatten a nested list of integers, accounting for potential nesting levels.

stack tree depth first search design queue

Problem Statement

You are given a nested list of integers, where each element is either an integer or a list. The list may contain other lists, which may contain integers or further nested lists. The goal is to implement an iterator that returns the elements of the list in a flattened order, allowing sequential access.

To solve this, implement the NestedIterator class. The iterator should support the methods hasNext() and next(), where hasNext() checks if there are any remaining elements to iterate, and next() returns the next integer in the flattened order. The implementation must efficiently handle any depth of nesting in the input list.

Examples

Example 1

Input: See original problem statement.

Output: See original problem statement.

initialize iterator with nestedList res = [] while iterator.hasNext() append iterator.next() to the end of res return res

Example 2

Input: nestedList = [[1,1],2,[1,1]]

Output: [1,1,2,1,1]

By calling next repeatedly until hasNext returns false, the order of elements returned by next should be: [1,1,2,1,1].

Example 3

Input: nestedList = [1,[4,[6]]]

Output: [1,4,6]

By calling next repeatedly until hasNext returns false, the order of elements returned by next should be: [1,4,6].

Constraints

1 <= nestedList.length <= 500
The values of the integers in the nested list is in the range [-106, 106].

Solution Approach

Stack-based Depth-First Search (DFS)

A stack can be used to keep track of the current position in the nested list. At each step, if the current element is a list, push it onto the stack; if it’s an integer, return it as the next element in the sequence.

Binary-Tree Traversal Pattern

The problem's pattern resembles binary-tree traversal in that you process one element (or 'node') and then recursively explore its sub-elements (or 'children'). This pattern is well-suited for handling the nested list structure efficiently.

State Tracking

To avoid recalculating the entire structure every time, you need to maintain the current state of the iterator. This involves checking whether the current element is an integer or list and appropriately managing the iteration state.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time and space complexity depend on the final approach. The space complexity is affected by the depth of the nesting in the list, while the time complexity is driven by how efficiently we can traverse the list and access its elements.

What Interviewers Usually Probe

The candidate demonstrates clear understanding of stack-based traversal.
The candidate can explain depth-first search principles and apply them to nested iteration.
The candidate exhibits efficient state tracking and recognizes when to process integers vs. lists.

Common Pitfalls or Variants

Common pitfalls

Failing to correctly handle deeply nested lists, causing inefficiencies in iteration.
Incorrectly maintaining state while traversing, leading to skipped or repeated elements.
Inefficient use of memory or stack space when dealing with lists of large or varied depth.

Follow-up variants

Handle scenarios with varying list sizes and depths to test scalability.
Implement a breadth-first search version of the iterator for comparison.
Modify the problem to work with more complex nested data types beyond integers.

FAQ

What is the core pattern in the Flatten Nested List Iterator problem?

The core pattern is binary-tree traversal with state tracking to efficiently flatten nested lists.

How does a stack-based approach work in this problem?

A stack is used to track nested sublists, allowing you to explore each element in a depth-first manner while maintaining the current position.

What are the key considerations when designing the NestedIterator class?

Key considerations include handling deep nesting, managing the iterator state, and ensuring efficient element retrieval with minimal overhead.

What is the time complexity for the Flatten Nested List Iterator?

The time complexity depends on the number of elements and the depth of nesting, but it can be considered linear in terms of the total number of elements.

How can I optimize space usage in the Flatten Nested List Iterator?

Space optimization can be achieved by minimizing the stack size and ensuring that only the necessary elements are stored during traversal.

terminal

Solution

Solution 1

#### Python3

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# """
# This is the interface that allows for creating nested lists.
# You should not implement it, or speculate about its implementation
# """
# class NestedInteger:
#    def isInteger(self) -> bool:
#        """
#        @return True if this NestedInteger holds a single integer, rather than a nested list.
#        """
#
#    def getInteger(self) -> int:
#        """
#        @return the single integer that this NestedInteger holds, if it holds a single integer
#        Return None if this NestedInteger holds a nested list
#        """
#
#    def getList(self) -> [NestedInteger]:
#        """
#        @return the nested list that this NestedInteger holds, if it holds a nested list
#        Return None if this NestedInteger holds a single integer
#        """


class NestedIterator:
    def __init__(self, nestedList: [NestedInteger]):
        def dfs(ls):
            for x in ls:
                if x.isInteger():
                    self.nums.append(x.getInteger())
                else:
                    dfs(x.getList())

        self.nums = []
        self.i = -1
        dfs(nestedList)

    def next(self) -> int:
        self.i += 1
        return self.nums[self.i]

    def hasNext(self) -> bool:
        return self.i + 1 < len(self.nums)


# Your NestedIterator object will be instantiated and called as such:
# i, v = NestedIterator(nestedList), []
# while i.hasNext(): v.append(i.next())

Continue Practicing

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