Python-Programming-Exercises-by-EKIKA/list.md

Python List Exercises

These exercises will help you get really good at using lists in Python. You'll practice things like loops, slicing, grouping, and working with nested lists.

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PYEKX-6280ca9a

Flatten a Deeply Nested List

Problem

Write a function flatten_list(nested_list) that flattens an arbitrarily nested list into a single-level list.

Example

flatten_list([1, [2, 3], [4, [5, 6], 7]])
# Output: [1, 2, 3, 4, 5, 6, 7]

Why? (Why it matters)

Flattening is common in:

• ETL pipelines
• JSON parsing
• Recursive DOM traversal

Pro Constraints

• Must support arbitrary levels of nesting.
• Must not mutate the input list.
• Use recursion or a stack-based approach.
• Bonus: Implement both recursive and iterative versions.

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PYEKX-1d45ec3e

Group Consecutive Duplicates

Problem

Write a function group_consecutive(lst) that groups only consecutive duplicate elements into sublists.

Example

group_consecutive([1, 1, 2, 2, 2, 3, 1, 1])
# Output: [[1, 1], [2, 2, 2], [3], [1, 1]]

Why?

This mimics:

• Run-length encoding
• Log event grouping
• Real-time batching by last-seen state

Pro Constraints

• Avoid using third-party libraries.
• Maintain original order.
• Think about how to handle empty input and non-integer elements.

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PYEKX-3c65900f

Rotate List (Bidirectional) k Times

Problem

Write a function rotate_list(lst, k) that rotates the list right by k steps.

Support negative k to rotate left.

Example

rotate_list([1, 2, 3, 4, 5], 2)
# Output: [4, 5, 1, 2, 3]

rotate_list([1, 2, 3, 4, 5], -2)
# Output: [3, 4, 5, 1, 2]

Why?

• Common in buffer management, scheduling, circular data structures.
• Reinforces modular math and slicing mastery.

Pro Constraints

• Solve it using slicing.
• Then try using only loops - no slicing.
• Handle large k values: rotate_list([1,2,3], 100).

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PYEKX-6a7bc172

List Difference (Ordered & Counted)

Problem

Write a function diff_lists(a, b) that removes one matching element in b for each occurrence found in a, preserving order.

Example

diff_lists([1, 2, 2, 3, 4], [2, 4])
# Output: [1, 2, 3]

Why?

• Models real-world inventory adjustments.
• Used in symmetric difference, record purging, filtering with quantity.

Pro Constraints

• Do not use collections.Counter.
• Optimize for readability and minimal mutations.
• Bonus: Add a flag to toggle whether duplicates in b remove all vs one.

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Pro Tips

• Try solving without using libraries.
• Then try versions using collections.Counter, itertools, or functools for practice.
• Write unit tests for edge cases:
  • Empty lists
  • Deep nesting
  • Large k values
  • Negative numbers

    def test_flatten():
        assert flatten_list([1, [2], [[3]]]) == [1, 2, 3]
        assert flatten_list([]) == []
        assert flatten_list([[], [[], [[]]]]) == []
  
    def test_rotate():
        assert rotate_list([1,2,3], 0) == [1,2,3]
        assert rotate_list([1,2,3], 3) == [1,2,3]
        assert rotate_list([1,2,3], -1) == [2,3,1]