How does Atlas ensure Polars test conventions are followed?

Atlas uses the `grep` tool to find existing `pytest (assert_frame_equal)` test files in your repository, then copies their framework, import style, and naming conventions when writing new Polars tests to ensure consistency.

Can Atlas run `pytest` for Polars tests?

Yes, Atlas uses the `bash` tool to execute `pytest` commands, including `pytest (assert_frame_equal)` for Polars, and reports failures, truncating large outputs for clarity while saving the full log.

How does Atlas handle dependencies for Polars testing?

Atlas integrates with `uv` to manage and run Polars project dependencies, ensuring the test environment is correctly set up before executing `pytest` commands via the `bash` tool.

What safety features does Atlas offer when writing Polars tests?

Atlas drafts a plan in a read-only agent, asks for permission before running tools, shows a unified diff for every file edit, and uses permission-gated tool calls to ensure safety and transparency in Polars test generation.

Does Atlas understand Polars `LazyFrame` operations?

Yes, Atlas is designed to read Polars `LazyFrame` chains, expression contexts, and where `collect()` materializes data, allowing it to generate relevant and optimized tests for your Polars code.

How does Atlas ensure new Polars test code is formatted correctly?

Atlas can run `ruff format` behind a permission prompt to automatically format new Polars test files, ensuring they conform to the project's established style guidelines and maintain code consistency.

What if a Polars module is very large and needs many tests?

For large Polars modules, Atlas can use the `todowrite` tool to keep track of progress, allowing you to iterate on test generation and ensure comprehensive coverage for all public functions and methods.

Can Atlas help optimize Polars query plans during testing?

Yes, Atlas can print `explain()` on Polars query plans and show which projections got pruned before and after optimizations, helping you generate tests that validate both correctness and efficiency of your `LazyFrame` operations.

Write Unit Tests for Untested Polars Code with Atlas in 2026

Atlas helps Polars developers write unit tests for untested code by reading existing modules, copying `pytest (assert_frame_equal)` conventions, and running tests with the `bash` tool, ensuring all new tests are executed and pass using `uv` for dependencies and `ruff format` for style in 2026.

How Atlas identifies and scopes Polars unit tests

Atlas identifies public functions in Polars modules using the `lsp` tool's `documentSymbol` operation to ensure 100% coverage for new unit tests. This process begins by reading the target module, typically a Python file containing Polars `LazyFrame` operations, to understand its structure.

To begin writing unit tests for an untested Polars module, Atlas first uses the `read` tool to ingest the module's source code. This allows Atlas to understand the module's logic, including how `LazyFrame` chains are constructed and where `collect()` calls materialize data. Following this, Atlas employs the `lsp` tool with its `documentSymbol` operation to enumerate all exported symbols, ensuring that no public function or method within the Polars module is overlooked during test generation. This comprehensive symbol listing is crucial for creating a complete test suite. Once the module's public interface is understood, Atlas then uses the `grep` tool to search for an existing test file within the repository. This step is vital for copying the repository's established `pytest (assert_frame_equal)` framework, import style, and naming conventions, ensuring that any new Polars tests direct integrate with the existing codebase. This approach guarantees that the generated tests are not only functional but also adhere to the project's specific Polars testing idioms.

Running and iterating on Polars unit tests with Atlas

Atlas executes new Polars unit tests using the `bash` tool, providing immediate feedback on failures and truncating output over 2000 lines or 50 KB for clarity. This ensures developers can quickly identify and address issues in their `pytest (assert_frame_equal)` suites.

After Atlas drafts a new Polars test file using the `write` tool, the next critical step is to run the tests. Atlas leverages the `bash` tool to execute the `pytest` command, specifically targeting the newly created test suite. This execution is where `pytest (assert_frame_equal)` comes into play, validating the correctness of Polars `DataFrame` and `LazyFrame` outputs. Atlas monitors the test run, capturing all output. If the output exceeds 2000 lines or 50 KB, it is truncated for readability in the terminal, with the full log saved to a file for detailed review. This immediate feedback loop is essential for an iterative development process. When tests fail, Atlas uses the `edit` tool to allow developers to modify the test file directly, making necessary corrections to the Polars expressions or assertions. For larger Polars modules that require extensive testing, Atlas can utilize the `todowrite` tool to maintain a list of pending tasks, ensuring that progress is tracked and all aspects of the module eventually receive comprehensive test coverage. This iterative cycle continues until the entire test suite is green, confirming the robustness of the Polars code.

Ensuring quality and safety in Polars test generation with Atlas

Atlas ensures new Polars unit tests adhere to repository standards by applying `ruff format` and presenting a unified diff for every file edit before writing, offering 100% transparency. This process is permission-gated, preventing unintended changes to your Polars codebase.

Atlas prioritizes both code quality and developer safety throughout the test generation process for Polars projects. Before any changes are written to disk, Atlas computes a unified diff for every proposed file edit, surfacing it for explicit approval. This allows Polars developers to review exactly what changes Atlas intends to make to their test files, including new `pytest (assert_frame_equal)` assertions or `LazyFrame` test cases. Every Atlas tool call, including `write` and `bash`, is permission-gated against `allow`, `ask`, and `deny` rules, providing granular control over its actions. Atlas drafts its initial plan in a read-only plan agent, asking for user confirmation before switching to a build agent that can modify files. Furthermore, Atlas can automatically apply `ruff format` to new Polars test files, ensuring they conform to the project's established style guidelines. This integration with `ruff format` behind a permission prompt guarantees that new tests are not only functional but also maintain code consistency. Atlas also snapshots file changes as git patches, allowing edits to be easily diffed and rolled back if needed, providing an additional layer of safety for your Polars development workflow.

Setting up Atlas for Polars-specific testing workflows

To effectively test Polars code, Atlas requires a `pyproject.toml` that pins Polars, allowing it to understand `LazyFrame` chains and `collect()` calls for optimal query plan analysis in 2026. This setup ensures Atlas can accurately interpret your data transformations.

For Atlas to provide maximum value in a Polars testing workflow, a specific setup is recommended. The project must have a `pyproject.toml` file that explicitly pins the Polars dependency. This configuration allows Atlas to correctly interpret Polars-specific constructs. With this setup, Atlas can read your `LazyFrame` chains, understand expression contexts, and identify precisely where each `.collect()` call materializes data. This deep understanding is crucial for generating relevant and efficient unit tests that target the core logic of your Polars operations. For instance, Atlas can be asked to convert a `scan_csv` operation combined with a `filter` into an optimized lazy chain, ensuring that predicate pushdown reaches the reader, a common Polars optimization. Furthermore, Atlas can print `explain()` on the query plan, showing which projections got pruned both before and after optimizations, providing valuable insights into the Polars query optimizer's behavior. This capability allows Atlas to generate tests that not only verify correctness but also validate performance characteristics, such as ensuring `pytest (assert_frame_equal)` checks on optimized `LazyFrame` outputs.

Step by step

01Use `atlas read src/my_polars_module.py` to ingest the Polars module's source code and understand its structure.
02Run `atlas lsp documentSymbol src/my_polars_module.py` to enumerate all public functions and methods in the Polars module.
03Execute `atlas grep "import polars as pl" tests/existing_test_file.py` to find an existing Polars test file and identify its `pytest (assert_frame_equal)` conventions.
04Draft the new Polars test file with `atlas write tests/test_my_polars_module.py`, reviewing the unified diff for approval.
05Run the newly generated Polars tests using `atlas bash "uv run pytest tests/test_my_polars_module.py"` to check for failures.
06Iterate on test fixes with `atlas edit tests/test_my_polars_module.py` until all `pytest (assert_frame_equal)` assertions pass.
07Apply Polars-specific formatting with `atlas bash "ruff format tests/test_my_polars_module.py"` to ensure style consistency.
08Stage and commit the new Polars test file using `atlas commit -m "feat: Add unit tests for my_polars_module"`.

Frequently asked questions

How does Atlas ensure Polars test conventions are followed?: Atlas uses the `grep` tool to find existing `pytest (assert_frame_equal)` test files in your repository, then copies their framework, import style, and naming conventions when writing new Polars tests to ensure consistency.
Can Atlas run `pytest` for Polars tests?: Yes, Atlas uses the `bash` tool to execute `pytest` commands, including `pytest (assert_frame_equal)` for Polars, and reports failures, truncating large outputs for clarity while saving the full log.
How does Atlas handle dependencies for Polars testing?: Atlas integrates with `uv` to manage and run Polars project dependencies, ensuring the test environment is correctly set up before executing `pytest` commands via the `bash` tool.
What safety features does Atlas offer when writing Polars tests?: Atlas drafts a plan in a read-only agent, asks for permission before running tools, shows a unified diff for every file edit, and uses permission-gated tool calls to ensure safety and transparency in Polars test generation.
Does Atlas understand Polars `LazyFrame` operations?: Yes, Atlas is designed to read Polars `LazyFrame` chains, expression contexts, and where `collect()` materializes data, allowing it to generate relevant and optimized tests for your Polars code.
How does Atlas ensure new Polars test code is formatted correctly?: Atlas can run `ruff format` behind a permission prompt to automatically format new Polars test files, ensuring they conform to the project's established style guidelines and maintain code consistency.
What if a Polars module is very large and needs many tests?: For large Polars modules, Atlas can use the `todowrite` tool to keep track of progress, allowing you to iterate on test generation and ensure comprehensive coverage for all public functions and methods.
Can Atlas help optimize Polars query plans during testing?: Yes, Atlas can print `explain()` on Polars query plans and show which projections got pruned before and after optimizations, helping you generate tests that validate both correctness and efficiency of your `LazyFrame` operations.

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