Reformatting with yapf

[korbit-ai]

User description

What do these changes do?

Related issue number

Resolves #54

• passes git diff upstream/master -u -- "*.py" | flake8 --diff

Description by Korbit AI

Note

This feature is in early access. You can enable or disable it in the Korbit Console.

What change is being made?

Integrate yapf for code formatting and reformat existing codebase using yapf.

Why are these changes being made?

To ensure consistent code style and improve readability across the codebase. Adding yapf to the linting process automates formatting, reducing manual effort and potential for style-related errors.

---

PR Type

enhancement, other

---

Description

• Integrated yapf for code formatting across the codebase to ensure consistent style and improve readability.
• Reformatted various files including dataframe.py, series.py, groupby.py, and multiple test files using yapf.
• Added yapf to the list of Python linting tools in Travis CI configuration.
• Updated .travis.yml to include yapf in the linting script.

---

Changes walkthrough 📝

	Relevant files
Formatting	17 files dataframe.py Codebase reformatted using `yapf` for consistent style      modin/pandas/dataframe.py Reformatted import statements for better readability. Adjusted function definitions and calls to improve code style. Enhanced code readability by reformatting long lines and aligning parameters. Applied consistent formatting across the entire file using yapf. +1417/-816 test_series.py Test case formatting adjustments for `to_csv` function      modin/pandas/test/test_series.py Reformatted a test case for to_csv to improve readability. Adjusted line breaks in the test function call. +2/-2      test_dataframe.py Code formatting improvements for test_dataframe.py              modin/pandas/test/test_dataframe.py Reformatted dictionary and list structures for better readability. Adjusted line breaks for function calls and assertions. Improved code consistency by aligning similar structures. +449/-331 series.py Code formatting improvements for series.py                              modin/pandas/series.py Reformatted method signatures for better readability. Adjusted line breaks for method definitions. Improved code consistency by aligning similar structures. +365/-94 groupby.py Code formatting improvements for groupby.py                            modin/pandas/groupby.py Reformatted method signatures and function calls for better readability. Adjusted line breaks for method definitions and lambda functions. Improved code consistency by aligning similar structures. +107/-82 test_groupby.py Code formatting improvements for test_groupby.py                  modin/pandas/test/test_groupby.py Reformatted dictionary structures for better readability. Adjusted line breaks for function calls and assertions. Improved code consistency by aligning similar structures. +41/-36  test_io.py Code formatting improvements for test_io.py                            modin/pandas/test/test_io.py Reformatted dictionary structures for better readability. Adjusted line breaks for function calls and assertions. Improved code consistency by aligning similar structures. +36/-36  test_concat.py Code formatting improvements for test_concat.py                    modin/pandas/test/test_concat.py Reformatted dictionary structures for better readability. Adjusted line breaks for function calls and assertions. Improved code consistency by aligning similar structures. +55/-46  pandas_code_gen.py Code formatting improvements for pandas_code_gen.py            modin/pandas/pandas_code_gen.py Reformatted string concatenations for better readability. Adjusted line breaks for function calls and string operations. Improved code consistency by aligning similar structures. +8/-9      concat.py Apply YAPF formatting to improve code readability in concat.py modin/pandas/concat.py Reformatted function definitions and calls to improve readability. Adjusted indentation for better alignment of parameters and arguments. Enhanced code consistency by applying uniform formatting. +59/-41  utils.py Code formatting improvements using YAPF in utils.py            modin/pandas/utils.py Removed unnecessary blank lines and added consistent spacing. Reformatted function calls and return statements for better clarity. Improved code alignment and readability with YAPF. +33/-23  index_metadata.py Enhance code style and readability with YAPF in index_metadata.py modin/pandas/index_metadata.py Reformatted import statements for compactness. Adjusted function definitions and calls for better readability. Applied consistent indentation and spacing throughout the file. +34/-21  indexing.py Improve code style and consistency using YAPF in indexing.py modin/pandas/indexing.py Reformatted import statements and function definitions. Improved alignment and spacing for better readability. Applied YAPF formatting to maintain consistent code style. +21/-19  reshape.py Apply YAPF formatting for better readability in reshape.py modin/pandas/reshape.py Reformatted function definitions and calls for clarity. Enhanced code readability with consistent indentation. Applied YAPF formatting to maintain uniform code style. +29/-13  datetimes.py Improve code readability using YAPF in datetimes.py            modin/pandas/datetimes.py Reformatted function definitions and calls for improved clarity. Enhanced code readability with consistent spacing and alignment. Applied YAPF formatting to ensure uniform code style. +47/-19  __init__.py Enhance module initialization readability with YAPF in __init__.py modin/pandas/init.py Reformatted import statements for better readability. Applied consistent indentation and spacing using YAPF. Improved code style and alignment in the module initialization. +13/-10  io.py Apply YAPF formatting for improved readability in io.py    modin/pandas/io.py Reformatted function calls and adjusted indentation. Improved code readability and consistency with YAPF. Enhanced alignment of parameters and arguments. +6/-6
Dependencies	1 files install-dependencies.sh Add yapf to linting tools in Travis CI                                      .travis/install-dependencies.sh Added yapf to the list of Python linting tools to be installed. +1/-1
Configuration changes	1 files .travis.yml Integrate YAPF into Travis CI linting process                        .travis.yml Added YAPF command to the linting script for code formatting. +1/-0

---

💡 PR-Agent usage:
Comment /help on the PR to get a list of all available PR-Agent tools and their descriptions

[korbit-ai]

Clone of the PR modin-project/modin#58

[korbit-ai]

My review is in progress 📖 - I will have feedback for you in a few minutes!

[coderabbitai]

Important

Review skipped

Auto reviews are disabled on this repository.

Please check the settings in the CodeRabbit UI or the .coderabbit.yaml file in this repository. To trigger a single review, invoke the @coderabbitai review command.

You can disable this status message by setting the reviews.review_status to false in the CodeRabbit configuration file.

---

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

Share

• X
• Mastodon
• Reddit
• LinkedIn

Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai generate interesting stats about this repository and render them as a table.
  • @coderabbitai show all the console.log statements in this repository.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (invoked as PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Additionally, you can add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.

[korbit-ai]

I have reviewed your code and found 6 potential issues.

[korbit-ai]

The reformatting of the outfile.write() calls in the code_gen_test() function has reduced readability. Consider using a multi-line string to improve clarity. For example:

outfile.write(f"""
    ray_{name} = create_test_{name}()

    with pytest.raises(NotImplementedError):
        ray_{name}.{func}""")

This approach maintains the original structure while improving readability and making future modifications easier.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[korbit-ai]

The test_df_concat function is currently defined as a fixture but appears to be intended as a test function. To ensure proper test execution, please remove the @pytest.fixture decorator from this function. If it's meant to be a fixture, consider renaming it and moving its content to an actual test function.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[korbit-ai]

Consider adding exception handling around the calls to pandas.get_dummies() in the get_dummies() and get_dummies_remote() functions. This will make the code more robust by catching and handling any exceptions that may occur due to invalid arguments or unexpected data formats.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[korbit-ai]

The to_datetime function is not handling the return value of pandas.to_datetime() when errors='raise'. This could lead to unexpected behavior as exceptions might be silently ignored. Consider capturing and handling the return value to ensure proper error propagation.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[korbit-ai]

The groupby method is missing a proper docstring. This makes it difficult for other developers to understand its purpose and how to use it correctly. Please add a comprehensive docstring that explains the method's functionality, its parameters, and what it returns.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[korbit-ai]

The reformatting of function definitions to have each parameter on a new line is inconsistent with the rest of the codebase and the original style. Please revert these changes to maintain consistency and improve readability.

Chat with Korbit by mentioning @korbit-ai, and give a 👍 or 👎 to help Korbit improve your reviews.

[furwellness]

/review

[codiumai-pr-agent-free]

PR Reviewer Guide 🔍

(Review updated until commit 032eb2f)

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Code Formatting The changes appear to be mostly code reformatting, with some lines being split into multiple lines for better readability. This may affect code review but does not change functionality. Code Formatting Similar to the dataframe file, the changes here are mostly code reformatting, splitting long lines into multiple lines. Functionality does not appear to be changed. Minor Changes Some minor changes to indentation and line breaks, but no significant functional changes.

[furwellness]

@coderabbitai full review

[coderabbitai]

Actions performed

Full review triggered.

[codiumai-pr-agent-free]

Persistent review updated to latest commit 032eb2f

[codiumai-pr-agent-free]

PR Code Suggestions ✨

Category	Suggestion	Score
Best practice	Rename the method to avoid confusion with the built-in __class__ attribute Consider using a more descriptive name for the class method. The current name might be confusing as it's typically a built-in attribute in Python. If this is intended to be a constructor, consider renaming it to new or a custom factory method. modin/pandas/series.py [61-68] -def __class__(self, - data=None, - index=None, - dtype=None, - name=None, - copy=False, - fastpath=False): +def create_series(self, + data=None, + index=None, + dtype=None, + name=None, + copy=False, + fastpath=False): raise NotImplementedError("Not Yet implemented.") Apply this suggestion Suggestion importance[1-10]: 9 Why: The suggestion correctly identifies a potential confusion with using __class__ as a method name, which is typically a built-in attribute in Python. Renaming it to avoid confusion is a best practice.	9
	Use context managers for exception testing to improve readability and ensure proper exception handling Consider using a context manager (with statement) for the pytest.raises assertions to improve readability and ensure proper exception handling. modin/pandas/test/test_dataframe.py [1327-1328] -pytest.raises(ValueError, ray_simple.drop, 5) -pytest.raises(ValueError, ray_simple.drop, 'C', 1) +with pytest.raises(ValueError): + ray_simple.drop(5) +with pytest.raises(ValueError): + ray_simple.drop('C', 1) Apply this suggestion Suggestion importance[1-10]: 8 Why: Using context managers for exception testing is a best practice that improves readability and ensures proper exception handling, making the code more robust.	8
	Use a more specific exception type for improved error handling Consider using a more specific exception type instead of the generic TypeError for better error handling and clarity. modin/pandas/dataframe.py [1413-1415] -raise TypeError("(\"'dict' object is not callable\", " - "'occurred at index {0}'".format( - self.index[0])) +raise ValueError("'dict' object is not callable for axis=1 operation at index {0}".format(self.index[0])) Apply this suggestion Suggestion importance[1-10]: 7 Why: Changing the exception type from TypeError to ValueError provides better clarity and specificity in error handling, which is a best practice for maintainable code.	7
	Use more descriptive variable names to improve code readability and maintainability Consider using a more descriptive variable name for frame_data. Since it's used in multiple test functions, a name like test_frame_data might be more appropriate. modin/pandas/test/test_dataframe.py [771-776] -frame_data = { +test_frame_data = { "col1": [0, 1, 2, 3], "col2": [4, 5, 6, 7], "col3": [8, 9, 0, 1], "col4": [2, 4, 5, 6] } Apply this suggestion Suggestion importance[1-10]: 7 Why: Using a more descriptive variable name improves code readability and maintainability, especially since frame_data is used across multiple test functions.	7
Enhancement	Use a more specific exception to indicate that the method should be implemented in a subclass Consider using a more specific exception instead of NotImplementedError. For example, you could use pandas.errors.AbstractMethodError to indicate that this method should be implemented in a subclass. modin/pandas/series.py [61-68] def __class__(self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False): - raise NotImplementedError("Not Yet implemented.") + raise pandas.errors.AbstractMethodError(self) Apply this suggestion Suggestion importance[1-10]: 8 Why: Using a more specific exception like pandas.errors.AbstractMethodError provides clearer intent that the method is abstract and should be implemented in subclasses, enhancing code clarity.	8
	Add type hints to method parameters for improved readability and type checking Consider using type hints for the method parameters. This can improve code readability and enable better static type checking. modin/pandas/series.py [61-68] +from typing import Optional, Any + def __class__(self, - data=None, - index=None, - dtype=None, - name=None, - copy=False, - fastpath=False): + data: Optional[Any] = None, + index: Optional[Any] = None, + dtype: Optional[Any] = None, + name: Optional[str] = None, + copy: bool = False, + fastpath: bool = False): raise NotImplementedError("Not Yet implemented.") Apply this suggestion Suggestion importance[1-10]: 7 Why: Adding type hints improves code readability and facilitates static type checking, which is beneficial for maintainability and reducing potential type-related errors.	7
	Use f-string for string formatting instead of .format() method Consider using an f-string for string formatting instead of the .format() method for improved readability and potentially better performance. modin/pandas/dataframe.py [1446-1448] -raise TypeError("(\"'list' object is not callable\", " - "'occurred at index {0}'".format( - self.index[0])) +raise TypeError(f"'list' object is not callable, occurred at index {self.index[0]}") Apply this suggestion Suggestion importance[1-10]: 5 Why: Using f-strings improves readability and is generally more efficient than the .format() method, but the change is not critical to the functionality of the code.	5
	Simplify the list of lambda functions by using built-in functions where possible and improving readability Consider using a list comprehension instead of multiple lambda functions for testfuncs. This can improve readability and potentially performance. modin/pandas/test/test_dataframe.py [51-54] testfuncs = [ - lambda x: x + 1, lambda x: str(x), lambda x: x * x, lambda x: x, - lambda x: False + (lambda x: x + 1), + str, + (lambda x: x * x), + (lambda x: x), + (lambda x: False) ] Apply this suggestion Suggestion importance[1-10]: 5 Why: The suggestion improves readability by using built-in functions where possible, but the performance gain is minimal. The existing code is already clear, so the change is not crucial.	5
	Simplify list creation using a single-line list comprehension Consider using a list comprehension instead of a map function for better readability and potentially improved performance. modin/pandas/dataframe.py [1454-1457] -new_index = [f_name if isinstance(f_name, string_types) else f_name.__name__ - for f_name in func] +new_index = [f_name if isinstance(f_name, string_types) else f_name.__name__ for f_name in func] Apply this suggestion Suggestion importance[1-10]: 3 Why: The suggestion simplifies the list comprehension by removing a newline, which is a minor readability improvement but does not significantly impact performance or functionality.	3
Maintainability	Use a more descriptive variable name to improve code clarity Consider using a more descriptive variable name instead of 'f_name' to improve code readability and maintainability. modin/pandas/dataframe.py [1454-1457] -new_index = [f_name if isinstance(f_name, string_types) else f_name.__name__ - for f_name in func] +new_index = [func_item if isinstance(func_item, string_types) else func_item.__name__ + for func_item in func] Apply this suggestion Suggestion importance[1-10]: 6 Why: Using a more descriptive variable name enhances code readability and maintainability, which is beneficial for understanding and maintaining the codebase.	6
	Add a TODO comment to track pending implementation Consider adding a TODO comment to indicate that this method needs implementation. This can help track which methods are still pending implementation. modin/pandas/series.py [61-68] def __class__(self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False): - raise NotImplementedError("Not Yet implemented.") + # TODO: Implement Series creation + raise NotImplementedError("Series creation not yet implemented.") Apply this suggestion Suggestion importance[1-10]: 5 Why: Adding a TODO comment is a minor improvement for maintainability, helping developers track pending tasks, but it does not address any critical issues.	5
Performance	Use a tuple instead of a list for constant values to improve performance and signal immutability Consider using a tuple instead of a list for keys since it appears to be a constant set of values. This can potentially improve performance and signal immutability. modin/pandas/test/test_dataframe.py [61] -keys = ['col1', 'col2', 'col3', 'col4'] +keys = ('col1', 'col2', 'col3', 'col4') Apply this suggestion Suggestion importance[1-10]: 6 Why: Using a tuple for constant values is a good practice for signaling immutability and can slightly improve performance. However, the impact is minor in this context.	6

[furwellness]

No comment from coderabbit