Add nest() and unnest() methods

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• Add nest() and unnest() functions to convert nested DataFrames into long format and vice versa.
• Add unit tests for the both functions to ensure their correctness and robustness.

[lasuk]

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Please review the enclosed python package module and unit tests. Comment on whether it aligns with python best practices and community standards. Files enclosed: - nesting.py - test_nest.py - test_unnest.py

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## `nesting.py` Module ### Documentation - The module docstring provides a concise description of the purpose of the module. - Each function (`nest` and `unnest`) includes detailed docstrings with parameters, return types, and examples, which align with Python best practices. ### Function Implementation - **`nest` Function**: - Validates the presence of the specified columns in the DataFrame. - Checks for the existence of the key column. - Handles empty DataFrames gracefully by returning an empty DataFrame. - Groups the DataFrame by non-nested columns and applies a nested structure. - Uses `apply` and `pd.concat` efficiently for nesting. - **`unnest` Function**: - Validates the existence of the key column. - Checks that the key column contains DataFrames. - Handles empty DataFrames gracefully by returning an empty DataFrame. - Uses `pd.concat` for unnesting the DataFrame efficiently. ### Code Quality - Uses list comprehensions and pandas functions efficiently. - Error messages are clear and informative. - No redundant code; the implementation is concise and readable. - Type hints are correctly used, improving code readability and maintenance. ### Examples - The examples provided in the docstrings are helpful for understanding the usage of the functions. - Including more complex examples or edge cases might improve the documentation further. ## `test_nest.py` and `test_unnest.py` Unit Tests ### Test Coverage - The unit tests cover a wide range of scenarios, including: - Basic functionality. - Edge cases such as empty DataFrames and missing columns. - Error handling for incorrect inputs. - The tests ensure that both `nest` and `unnest` functions are tested thoroughly. ### Test Implementation - Uses `pytest` for testing, which is a widely accepted framework in the Python community. - Each test function is well-named, indicating the specific case it tests. - `pd.testing.assert_frame_equal` is used to compare DataFrames, ensuring that the results are accurate. - Tests for error conditions use `pytest.raises` to verify that appropriate exceptions are raised. ### Code Quality - The test code is clean, with each test function focused on a specific aspect of the functionality. - Test data is created within the test functions, making them self-contained and easier to understand.

Overall Assessment

Alignment with Python Best Practices

• The module and unit tests align well with Python best practices. The code is clean, well-documented, and efficiently written.
• The use of type hints, informative error messages, and comprehensive docstrings enhances the readability and maintainability of the code.

Community Standards

• The implementation follows common patterns in the pandas community for data manipulation tasks.
• The use of pytest and the structure of the unit tests adhere to widely accepted standards in the Python testing community.

Recommendations

Additional Documentation

• Consider adding more complex examples or edge cases in the docstrings to further illustrate the functions' capabilities.

Edge Cases in Testing

• While the tests are comprehensive, including additional edge cases such as DataFrames with multi-level columns or indexes might be beneficial.

Overall, the nesting.py module and its corresponding unit tests are well-crafted and adhere to Python best practices and community standards. The code is efficient, readable, and maintainable, with thorough test coverage ensuring reliability.