In 2026, Atlas enables Polars developers to rename functions, classes, or constants across an entire repository with unparalleled accuracy, leveraging `lsp` for authoritative references, `grep` for non-code occurrences, and `edit` for precise mechanical changes, all while integrating direct with `pytest (assert_frame_equal)` and `ruff format`.
How does Atlas rename Polars functions and classes precisely?
Atlas ensures precise renaming of Polars symbols by combining three powerful tools: `lsp`, `grep`, and `edit`. This hybrid approach, refined over 5 years, guarantees that every instance of a function, class, or constant is correctly identified and updated, even within complex Polars `LazyFrame` chains.
Renaming a symbol in a Polars codebase requires more than a simple text search; it demands an understanding of the code's semantic structure. Atlas addresses this by first employing the `lsp` tool's `findReferences` operation. This leverages the language server to provide an authoritative list of all true references to the symbol, accurately navigating Polars' expression API and `LazyFrame` contexts. Next, Atlas runs `grep` for the old symbol name. This step is crucial for catching occurrences outside the type system, such as strings, comments, documentation, or configuration files that the language server might overlook. Finally, the mechanical renames are applied using the `edit` tool with its `replaceAll` capability. A key safety feature of `edit` is its refusal of ambiguous single replacements; if it finds multiple matches for a string in a file where only one was intended, it will throw an error rather than silently corrupting the code, ensuring that unintended matches from `grep` do not lead to incorrect changes.
What Polars commands and files does Atlas use for refactoring?
Atlas integrates directly with the Polars developer's existing toolchain, recognizing `pyproject.toml` for project context and executing real commands like `pytest (assert_frame_equal)` and `ruff format`. This ensures that refactoring operations are not only accurate but also maintain the integrity and style of your Polars codebase in 2026.
For Polars projects, Atlas is designed to be a native extension of your workflow. It starts by reading your `pyproject.toml` file to understand project dependencies and configurations, which is essential for correctly interpreting Polars `LazyFrame` chains and expression contexts. for verification, Atlas doesn't just 'run tests'; it executes the actual Polars test runner command: `atlas bash pytest --assert-frame-equal`. This ensures that any refactoring, such as renaming a function used in a `DataFrame` transformation, is immediately validated against your comprehensive test suite, specifically checking for frame equality. Post-refactoring, maintaining code style is critical. Atlas facilitates this by allowing you to run `atlas bash ruff format .`, applying your project's `ruff format` rules to ensure all changes adhere to your established coding standards. For package management, Atlas is aware of `uv`, allowing it to operate within environments managed by the modern Polars package manager.
How does Atlas ensure safe refactoring and code review for Polars?
Atlas prioritizes safety and transparency throughout the refactoring process, especially when dealing with complex Polars queries. Every Atlas tool call is permission-gated, and all proposed changes are presented as unified diffs for your approval, preventing unintended modifications to your 2026 Polars codebase.
The safety mechanisms within Atlas are designed to give Polars developers complete control and confidence during refactoring. Before any tool executes a change, Atlas drafts a plan in a read-only plan agent, asking for your explicit permission before switching to a build agent. This permission-gated approach means you are always aware of and approve the actions Atlas intends to take. For every file edit, Atlas computes a unified diff, which is then surfaced for your review and approval. This allows you to meticulously inspect every proposed change, ensuring that a renamed symbol in a Polars `with_columns` expression or a `group_by` aggregation is exactly as intended. Furthermore, Atlas reads `git` branches, status, and diffs, and can stage and create commits on your behalf, integrating direct into your version control workflow. It also snapshots file changes as `git` patches, providing a robust rollback mechanism if any approved change proves problematic, offering a high degree of safety for critical Polars applications.
What is the full workflow to rename a Polars symbol with Atlas?
The complete workflow for renaming a Polars symbol with Atlas involves 4 core steps: identifying references, catching non-code occurrences, applying mechanical changes, and rigorous testing. This structured approach ensures a comprehensive and verified refactor, ready for your 2026 production environment.
Renaming a symbol in a Polars project with Atlas follows a clear, auditable process. First, you initiate the `lsp` tool's `findReferences` operation on the old symbol name. This provides the authoritative list of all semantic usages from the language server, crucial for navigating Polars' `LazyFrame` and `DataFrame` operations. Second, you run `grep` for the old name to catch any occurrences outside the type system, such as comments, string literals, or documentation that might reference the symbol. Third, the mechanical renames are applied using the `edit` tool's `replaceAll` function. This tool is designed for precision, refusing ambiguous single replacements to prevent accidental corruption. Finally, after the changes are applied, you compile and test your Polars project using `atlas bash pytest --assert-frame-equal`. A final `grep` for the old name serves as a definitive proof that zero remaining hits exist, confirming the refactor's completeness. This systematic workflow ensures that your Polars codebase remains functional and consistent after the rename.
Step by step
- 01Initialize Atlas in your Polars project, ensuring `pyproject.toml` is present and correctly configured.
- 02Execute `atlas lsp findReferences <old_symbol_name>` to gather all semantic references within your Polars codebase, including `LazyFrame` chains.
- 03Run `atlas grep <old_symbol_name>` to identify non-code occurrences in comments, strings, or documentation relevant to Polars.
- 04Apply mechanical renames using `atlas edit replaceAll <old_symbol_name> <new_symbol_name>` across the identified files, leveraging `edit`'s precision.
- 05Validate the refactoring by running your Polars test suite with `atlas bash pytest --assert-frame-equal` to ensure data frame integrity.
- 06Ensure code style consistency by applying `ruff format` with `atlas bash ruff format .` to your Polars files.
- 07Perform a final verification with `atlas grep <old_symbol_name>` to confirm zero remaining hits of the old symbol in your project.
- 08Review the unified diffs presented by Atlas and approve the changes, then `atlas git commit` to finalize the refactoring.
Frequently asked questions
- How does Atlas handle Polars' expression API during renames?
- Atlas leverages the `lsp` tool to understand the Abstract Syntax Tree (AST) declarations and semantic context of Polars' expression API, ensuring renames correctly propagate through `LazyFrame` chains and complex query plans.
- Can Atlas rename a Polars function used in a `scan_csv` chain?
- Yes, Atlas uses `lsp` to trace references through `scan_csv` and other Polars I/O operations, ensuring the function name is updated even within lazy query plans where predicate pushdown might occur.
- What if `grep` finds a false positive for a Polars symbol name?
- Atlas's `edit` tool refuses ambiguous single replacements, requiring explicit context or the `replaceAll` option for specific files, effectively preventing unintended changes from `grep` false positives in your Polars project.
- How does Atlas ensure my Polars tests still pass after a rename?
- After applying renames, Atlas prompts you to run `atlas bash pytest --assert-frame-equal`, allowing immediate verification of your Polars test suite and ensuring `DataFrame` equality is maintained.
- Does Atlas support `pyproject.toml` for Polars project configuration?
- Yes, Atlas reads your `pyproject.toml` to understand your Polars project's dependencies and configuration, providing relevant context for accurate refactoring operations.
- Can Atlas format my Polars code with `ruff format` after renaming?
- Absolutely. Atlas integrates `ruff format`, allowing you to run `atlas bash ruff format .` to automatically apply formatting rules to your Polars codebase post-refactoring, maintaining consistent style.
- How does Atlas prevent accidental changes to Polars code?
- Atlas prevents accidental changes through permission-gated tool calls, a read-only plan agent, and by presenting all proposed edits as unified diffs for your explicit approval before writing to disk.
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