Update 'list.md'
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list.md
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list.md
@@ -5,107 +5,130 @@ These exercises will help you get really good at using lists in Python. You'll p
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---
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## Exercise 1
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## PYEKX-6280ca9a
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**Flatten a Nested List**
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**Flatten a Deeply Nested List**
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### Problem
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Write a function `flatten_list(nested_list)` that takes a list which may contain nested lists of any depth, and returns a single, flat list.
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Write a function `flatten_list(nested_list)` that flattens an arbitrarily nested list into a single-level list.
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### Example
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```python
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flatten_list([1, [2, 3], [4, [5, 6], 7]])
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# Output: [1, 2, 3, 4, 5, 6, 7]
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```
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### Concepts
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### Why? (*Why it matters*)
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Flattening is common in:
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- ETL pipelines
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- JSON parsing
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- Recursive DOM traversal
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- Recursion
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- `isinstance` type checking
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- `extend` vs `append`
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### Pro Constraints
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- Must support arbitrary levels of nesting.
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- Must not mutate the input list.
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- Use recursion or a stack-based approach.
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- Bonus: Implement both recursive and iterative versions.
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---
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## Exercise 2
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## PYEKX-1d45ec3e
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**Group Consecutive Duplicates**
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### Problem
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Write a function `group_consecutive(lst)` that groups consecutive identical elements into sublists.
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Write a function `group_consecutive(lst)` that groups **only consecutive** duplicate elements into sublists.
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### Example
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```python
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group_consecutive([1, 1, 2, 2, 2, 3, 1, 1])
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# Output: [[1, 1], [2, 2, 2], [3], [1, 1]]
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```
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### Concepts
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### Why?
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This mimics:
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- Run-length encoding
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- Log event grouping
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- Real-time batching by last-seen state
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- Iteration with state tracking
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- Temporary sublists
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- `for` loops with conditionals
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### Pro Constraints
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- Avoid using third-party libraries.
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- Maintain original order.
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- Think about how to handle empty input and non-integer elements.
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---
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## Exercise 3
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## PYEKX-3c65900f
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**Rotate List k Times**
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**Rotate List (Bidirectional) k Times**
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### Problem
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Write a function `rotate_list(lst, k)` that rotates the list **right** by `k` steps.
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> BONUS: Support negative `k` to rotate left.
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> Support negative `k` to rotate left.
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### Example
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```python
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rotate_list([1, 2, 3, 4, 5], 2)
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# Output: [4, 5, 1, 2, 3]
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rotate_list([1, 2, 3, 4, 5], -2)
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# Output: [3, 4, 5, 1, 2]
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```
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### Concepts
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### Why?
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- Common in **buffer management, scheduling, circular data** structures.
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- Reinforces modular math and slicing mastery.
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- Identify concepts yourself.
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### Pro Constraints
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- Solve it using slicing.
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- Then try using only loops - no slicing.
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- Handle large `k` values: `rotate_list([1,2,3], 100)`.
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---
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## Exercise 4
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## PYEKX-6a7bc172
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**List Difference by Value and Order**
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**List Difference (Ordered & Counted)**
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### Problem
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Write a function `diff_lists(a, b)` that removes **each occurrence** in `b` from `a`, respecting the number of times elements occur.
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Write a function `diff_lists(a, b)` that removes one matching element in `b` for each occurrence found in `a`, preserving order.
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### Example
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```python
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diff_lists([1, 2, 2, 3, 4], [2, 4])
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# Output: [1, 2, 3]
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```
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> One `2` and the `4` are removed. The second `2` remains.
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### Why?
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- Models real-world inventory adjustments.
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- Used in symmetric difference, record purging, filtering with quantity.
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### Pro Constraints
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- Do not use `collections.Counter`.
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- Optimize for readability and minimal mutations.
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- Bonus: Add a flag to toggle whether duplicates in b remove all vs one.
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### Concepts
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- Element-by-element removal
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- List copying
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- `in` with deletion logic
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---
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## ✅ Pro Tips
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## Pro Tips
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- Try solving without using libraries.
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- Then try versions using `collections.Counter`, `itertools`, or `functools` for practice.
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- Write unit tests for edge cases:
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- Empty lists
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- Deep nesting
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- Large `k` values
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- Negative numbers
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```python
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def test_flatten():
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assert flatten_list([1, [2], [[3]]]) == [1, 2, 3]
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assert flatten_list([]) == []
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assert flatten_list([[], [[], [[]]]]) == []
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def test_rotate():
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assert rotate_list([1,2,3], 0) == [1,2,3]
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assert rotate_list([1,2,3], 3) == [1,2,3]
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assert rotate_list([1,2,3], -1) == [2,3,1]
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```
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