Create sorting script #13
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WalkthroughA new Python module named Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant sorting.py
User->>sorting.py: Call bubble_sort(arr)
User->>sorting.py: Call selection_sort(arr)
User->>sorting.py: Call insertion_sort(arr)
User->>sorting.py: Call merge_sort(arr)
sorting.py->>sorting.py: merge(left, right) (from merge_sort)
User->>sorting.py: Use .sort() and sorted() for comparison
sequenceDiagram
participant Script
participant Env
participant FileSystem
participant JSONParser
Script->>Env: Read GITHUB_EVENT_NAME, GITHUB_EVENT_PATH
Script->>FileSystem: Check if event file exists
alt File exists
Script->>FileSystem: Open event file (UTF-8)
FileSystem->>Script: File content
Script->>JSONParser: Parse JSON content
JSONParser->>Script: Parsed event data
Script->>Script: Return event data
else File missing or error
Script->>Script: Print error and exit
end
Script->>User: Print event name and event JSON payload
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Poem
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Summary of Changes
Hello @atharvsabdeai, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a new Python script that implements and showcases several fundamental sorting algorithms. The script provides clear, runnable examples for each algorithm, allowing users to easily understand their behavior and compare them with Python's built-in sorting capabilities.
Highlights
- New Script for Sorting Algorithms: A new Python file,
sorting.py, has been added to the repository, dedicated to demonstrating various sorting algorithms. - Multiple Sorting Implementations: The script includes distinct, self-contained implementations for Bubble Sort, Selection Sort, Insertion Sort, and Merge Sort (along with its
mergehelper function). - Algorithm Demonstration and Comparison: Each implemented sorting algorithm is demonstrated with a sample list, and its sorted output is printed. The script also includes examples of Python's native
list.sort()andsorted()functions for comparison.
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Code Review
This pull request introduces a new Python script with implementations of several common sorting algorithms. The implementations are functionally correct. My review provides feedback on improving the script's reusability, maintainability, and efficiency. Key suggestions include adding a __main__ guard, refactoring the repetitive test code, adding type hints for clarity, and noting a memory-efficiency improvement for the merge sort algorithm.
| # Sample input | ||
| original = [6, 6, 2, 3, 2] | ||
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| print("Original list:") | ||
| print(original) | ||
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| # Bubble Sort | ||
| arr1 = original.copy() | ||
| bubble_sort(arr1) | ||
| print("\nBubble Sort result:") | ||
| print(arr1) | ||
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| # Selection Sort | ||
| arr2 = original.copy() | ||
| selection_sort(arr2) | ||
| print("\nSelection Sort result:") | ||
| print(arr2) | ||
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| # Insertion Sort | ||
| arr3 = original.copy() | ||
| insertion_sort(arr3) | ||
| print("\nInsertion Sort result:") | ||
| print(arr3) | ||
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| # Merge Sort (not in-place) | ||
| arr4 = original.copy() | ||
| arr4_sorted = merge_sort(arr4) | ||
| print("\nMerge Sort result:") | ||
| print(arr4_sorted) | ||
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| # Python built-in sort (in-place) | ||
| arr5 = original.copy() | ||
| arr5.sort() | ||
| print("\nPython .sort() result:") | ||
| print(arr5) | ||
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| # Python built-in sorted() (returns new list) | ||
| arr6 = sorted(original) | ||
| print("\nPython sorted() result (non-in-place):") | ||
| print(arr6) |
There was a problem hiding this comment.
This block of code has two issues that should be addressed:
- Critical: The demonstration code runs in the global scope. This is a significant issue because if another module imports this file, the demonstration code will execute unexpectedly. All script execution logic should be placed within an
if __name__ == "__main__":block to ensure the module is reusable. - Medium: The code for testing each sorting algorithm is highly repetitive. This violates the Don't Repeat Yourself (DRY) principle and makes the code harder to maintain or extend. Refactoring this into a loop would make it much cleaner.
Here is a suggested refactoring that addresses both points by organizing the tests and placing them inside a __main__ guard:
if __name__ == "__main__":
# Sample input
original = [6, 6, 2, 3, 2]
print("Original list:")
print(original)
# Sorting functions to test: (name, function, is_in_place)
sorting_algorithms = [
("Bubble Sort", bubble_sort, True),
("Selection Sort", selection_sort, True),
("Insertion Sort", insertion_sort, True),
("Merge Sort", merge_sort, False),
("Python .sort()", lambda arr: arr.sort(), True),
("Python sorted()", sorted, False),
]
for name, sort_func, is_in_place in sorting_algorithms:
arr_copy = original.copy()
print(f"\n{name} result:")
if is_in_place:
sort_func(arr_copy)
print(arr_copy)
else:
sorted_arr = sort_func(arr_copy)
print(sorted_arr)| @@ -0,0 +1,121 @@ | |||
| def bubble_sort(arr): | |||
There was a problem hiding this comment.
To improve code clarity, maintainability, and enable static analysis, consider adding type hints to your function signatures, as recommended by PEP 484. This should be applied to all functions in this file.
For example, bubble_sort takes a list and modifies it in-place, so its return type is None. You can use the built-in list type for basic hinting. For more specific types (e.g., a list of numbers), you might use from typing import List, Union and annotate as List[Union[int, float]].
| def bubble_sort(arr): | |
| def bubble_sort(arr: list) -> None: |
| left = merge_sort(arr[:mid]) | ||
| right = merge_sort(arr[mid:]) |
There was a problem hiding this comment.
The current implementation of merge_sort creates new sub-lists using slicing (arr[:mid] and arr[mid:]) in each recursive call. For large lists, this can be inefficient and consume a lot of memory because Python creates a copy of the data for each slice. A more memory-efficient approach for merge_sort is to pass indices (e.g., start and end) to the recursive calls. This allows them to work on views of the original list (or a single copy) instead of creating many small, temporary copies.
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Actionable comments posted: 0
🧹 Nitpick comments (2)
sorting.py (2)
17-28: Optimize to avoid unnecessary swaps.The selection sort implementation is correct, but it performs unnecessary swaps when the minimum element is already in the correct position.
Consider adding a condition to avoid swapping when
min_idx == i:- arr[i], arr[min_idx] = arr[min_idx], arr[i] + if min_idx != i: + arr[i], arr[min_idx] = arr[min_idx], arr[i]
82-122: Good demonstration code with minor formatting fix needed.The sample usage effectively demonstrates all sorting algorithms and properly preserves the original array using
copy(). The comparison with Python's built-in sorting functions adds educational value.Add a newline at the end of the file for better formatting:
print("Python sorted() result (non-in-place):") print(arr6) +
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🔇 Additional comments (4)
sorting.py (4)
1-14: LGTM! Well-implemented bubble sort with optimization.The bubble sort implementation is correct and includes the early exit optimization when no swaps occur, which improves best-case performance from O(n²) to O(n).
31-42: LGTM! Correct insertion sort implementation.The insertion sort implementation is accurate and follows the standard algorithm correctly with proper bounds checking and element shifting.
45-57: LGTM! Correct merge sort implementation.The merge sort implementation is accurate with proper base case handling and recursive structure. The algorithm correctly maintains O(n log n) time complexity.
60-79: LGTM! Correct merge helper function.The merge function correctly implements the two-pointer technique with proper handling of remaining elements and maintains stability by using
<=in the comparison.
🤖 Refacto.AI: PR Summary & Reviewer GuideWarning: Requirements context (e.g., linked ticket) was not provided. Analysis is based solely on PR metadata and code diff, which may be incomplete. 🎯 1. The Goal (Why was this PR raised?)This PR aims to create a new Python script that implements and demonstrates various sorting algorithms. The implementation includes bubble sort, selection sort, insertion sort, and merge sort, along with examples of Python's built-in sorting methods. 🗺️ 2. The Changes (What was implemented?)
🤔 3. Key Areas for Human ReviewArea of Concern: Merge Sort Implementation
Area of Concern: Algorithm Correctness
Area of Concern: Early Termination in Bubble Sort
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Actionable comments posted: 0
🧹 Nitpick comments (1)
main.py (1)
5-27: Excellent refactoring with comprehensive error handling.The
load_github_event()function effectively separates concerns and provides robust error handling with clear, user-friendly messages. The implementation correctly validates environment variables, file existence, and handles JSON parsing errors.Consider refactoring to raise exceptions instead of calling
sys.exit()directly within the function, which would make it more testable and reusable:def load_github_event(): event_name = os.getenv("GITHUB_EVENT_NAME", "UNKNOWN_EVENT") event_path = os.getenv("GITHUB_EVENT_PATH") print(f"📦 Received GitHub event: {event_name}") if not event_path: - print("⚠️ Environment variable GITHUB_EVENT_PATH is not set. Cannot read event data.") - sys.exit(1) + raise ValueError("Environment variable GITHUB_EVENT_PATH is not set. Cannot read event data.") if not os.path.isfile(event_path): - print(f"❌ Event file not found at: {event_path}") - sys.exit(1) + raise FileNotFoundError(f"Event file not found at: {event_path}") try: with open(event_path, "r", encoding="utf-8") as f: return json.load(f) except json.JSONDecodeError as e: - print(f"❌ Failed to parse event JSON: {e}") + raise ValueError(f"Failed to parse event JSON: {e}") from e except Exception as e: - print(f"❌ Unexpected error reading event file: {e}") - - sys.exit(1) + raise RuntimeError(f"Unexpected error reading event file: {e}") from eThen handle the exceptions in
main()with appropriate error messages and exit codes.
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🔇 Additional comments (3)
main.py (3)
3-3: LGTM!The
sysimport is correctly added and necessary for thesys.exit()calls in error handling.
29-32: Clean and focused main function.The refactored
main()function is well-structured, focusing solely on the primary workflow while delegating event loading to the dedicated function. The JSON pretty-printing withindent=2improves readability.
35-35: LGTM!The main execution follows the standard Python idiom correctly.
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| # Python built-in sorted() (returns new list) | ||
| arr6 = sorted(original) | ||
| print("\nPython sorted() result (non-in-place):") | ||
| print(arr6) |
There was a problem hiding this comment.
This block of code has two issues that should be addressed:
- Critical: The demonstration code runs in the global scope. This is a significant issue because if another module imports this file, the demonstration code will execute unexpectedly. All script execution logic should be placed within an
if __name__ == "__main__":block to ensure the module is reusable. - Medium: The code for testing each sorting algorithm is highly repetitive. This violates the Don't Repeat Yourself (DRY) principle and makes the code harder to maintain or extend. Refactoring this into a loop would make it much cleaner.
Here is a suggested refactoring that addresses both points by organizing the tests and placing them inside a __main__ guard:
if __name__ == "__main__":
# Sample input
original = [6, 6, 2, 3, 2]
print("Original list:")
print(original)
# Sorting functions to test: (name, function, is_in_place)
sorting_algorithms = [
("Bubble Sort", bubble_sort, True),
("Selection Sort", selection_sort, True),
("Insertion Sort", insertion_sort, True),
("Merge Sort", merge_sort, False),
("Python .sort()", lambda arr: arr.sort(), True),
("Python sorted()", sorted, False),
]
for name, sort_func, is_in_place in sorting_algorithms:
arr_copy = original.copy()
print(f"\n{name} result:")
if is_in_place:
sort_func(arr_copy)
print(arr_copy)
else:
sorted_arr = sort_func(arr_copy)
print(sorted_arr)
|
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Refacto is reviewing this PR. Please wait for the review comments to be posted. |
| # Python built-in sorted() (returns new list) | ||
| arr6 = sorted(original) | ||
| print("\nPython sorted() result (non-in-place):") | ||
| print(arr6) |
There was a problem hiding this comment.
This block of code has two issues that should be addressed:
- Critical: The demonstration code runs in the global scope. This is a significant issue because if another module imports this file, the demonstration code will execute unexpectedly. All script execution logic should be placed within an
if __name__ == "__main__":block to ensure the module is reusable. - Medium: The code for testing each sorting algorithm is highly repetitive. This violates the Don't Repeat Yourself (DRY) principle and makes the code harder to maintain or extend. Refactoring this into a loop would make it much cleaner.
Here is a suggested refactoring that addresses both points by organizing the tests and placing them inside a __main__ guard:
if __name__ == "__main__":
# Sample input
original = [6, 6, 2, 3, 2]
print("Original list:")
print(original)
# Sorting functions to test: (name, function, is_in_place)
sorting_algorithms = [
("Bubble Sort", bubble_sort, True),
("Selection Sort", selection_sort, True),
("Insertion Sort", insertion_sort, True),
("Merge Sort", merge_sort, False),
("Python .sort()", lambda arr: arr.sort(), True),
("Python sorted()", sorted, False),
]
for name, sort_func, is_in_place in sorting_algorithms:
arr_copy = original.copy()
print(f"\n{name} result:")
if is_in_place:
sort_func(arr_copy)
print(arr_copy)
else:
sorted_arr = sort_func(arr_copy)
print(sorted_arr)
| @@ -0,0 +1,121 @@ | |||
| def bubble_sort(arr): | |||
There was a problem hiding this comment.
To improve code clarity, maintainability, and enable static analysis, consider adding type hints to your function signatures, as recommended by PEP 484. This should be applied to all functions in this file.
For example, bubble_sort takes a list and modifies it in-place, so its return type is None. You can use the built-in list type for basic hinting. For more specific types (e.g., a list of numbers), you might use from typing import List, Union and annotate as List[Union[int, float]].
| def bubble_sort(arr): | |
| def bubble_sort(arr: list) -> None: |
|
|
||
| mid = len(arr) // 2 | ||
| left = merge_sort(arr[:mid]) | ||
| right = merge_sort(arr[mid:]) |
There was a problem hiding this comment.
The current implementation of merge_sort creates new sub-lists using slicing (arr[:mid] and arr[mid:]) in each recursive call. For large lists, this can be inefficient and consume a lot of memory because Python creates a copy of the data for each slice. A more memory-efficient approach for merge_sort is to pass indices (e.g., start and end) to the recursive calls. This allows them to work on views of the original list (or a single copy) instead of creating many small, temporary copies.
| # Python built-in sorted() (returns new list) | ||
| arr6 = sorted(original) | ||
| print("\nPython sorted() result (non-in-place):") | ||
| print(arr6) |
There was a problem hiding this comment.
This block of code has two issues that should be addressed:
- Critical: The demonstration code runs in the global scope. This is a significant issue because if another module imports this file, the demonstration code will execute unexpectedly. All script execution logic should be placed within an
if __name__ == "__main__":block to ensure the module is reusable. - Medium: The code for testing each sorting algorithm is highly repetitive. This violates the Don't Repeat Yourself (DRY) principle and makes the code harder to maintain or extend. Refactoring this into a loop would make it much cleaner.
Here is a suggested refactoring that addresses both points by organizing the tests and placing them inside a __main__ guard:
if __name__ == "__main__":
# Sample input
original = [6, 6, 2, 3, 2]
print("Original list:")
print(original)
# Sorting functions to test: (name, function, is_in_place)
sorting_algorithms = [
("Bubble Sort", bubble_sort, True),
("Selection Sort", selection_sort, True),
("Insertion Sort", insertion_sort, True),
("Merge Sort", merge_sort, False),
("Python .sort()", lambda arr: arr.sort(), True),
("Python sorted()", sorted, False),
]
for name, sort_func, is_in_place in sorting_algorithms:
arr_copy = original.copy()
print(f"\n{name} result:")
if is_in_place:
sort_func(arr_copy)
print(arr_copy)
else:
sorted_arr = sort_func(arr_copy)
print(sorted_arr)
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