# Review a Pull Request in Pandas with Atlas in 2026 > Atlas helps Pandas developers in 2026 review pull requests by providing deep context, catching subtle bugs like chained assignment and ensuring robust code with `pytest (assert_frame_equal)`. Atlas empowers Pandas developers in 2026 to review pull requests with unprecedented depth, moving beyond simple line-by-line diffs to catch subtle, performance-critical bugs. By integrating directly with your Pandas toolchain,leveraging `pytest (assert_frame_equal)` for robust testing, `uv` for dependency management, and `ruff format` for consistent code style,Atlas ensures that every change is thoroughly vetted. It fetches the full context of modified files, identifies potential API breakages using LSP, and proactively flags common Pandas pitfalls like chained assignment or inefficient row-wise `apply` calls, providing a comprehensive review that a human might miss. ## Key takeaways - Atlas provides deep context for Pandas PRs, catching subtle bugs beyond the diff. - Leverage `pytest (assert_frame_equal)` and `ruff format` directly within Atlas for Pandas. - Atlas identifies Pandas API breakages using LSP `findReferences` across the codebase. - Proactively `grep` for stale Pandas patterns like old constants or inefficient `df.apply` calls. - Atlas's plan agent and unified diff ensure safe, transparent Pandas code modifications. ## How Atlas Fetches and Diffs Pandas Pull Requests In 2026, Atlas streamlines the initial phase of reviewing a Pandas pull request by directly interacting with your Git repository. It uses its VCS layer to fetch the specific branch and generate a comprehensive diff, providing a foundational view of all 10 changed files and their modifications. Atlas leverages its integrated VCS layer to manage Git operations, allowing you to fetch the specific branch associated with a Pandas pull request. Instead of relying on external commands, Atlas exposes `status`, `diff`, `diffRaw`, and `commits` directly over the same Git data. This capability ensures that you can quickly obtain the raw patch and identify all modified files within your Pandas project. For instance, if a PR modifies `pandas/core/frame.py` or `pandas/tests/frame/test_indexing.py`, Atlas will present a unified diff, preparing the ground for a deeper, context-aware review. This initial step is crucial for understanding the scope of changes before diving into the specifics of Pandas DataFrame transformations or index handling. ## Gaining Deep Context for Pandas Code Review To catch subtle Pandas bugs like chained assignment, Atlas goes beyond simple diff hunks, reading the full content of changed files. This approach ensures that you see the surrounding 20-30 lines of code, providing crucial context often missed in a line-by-line review, especially when dealing with complex DataFrame operations. A common pitfall in reviewing Pandas code is missing context outside the immediate diff hunks, which can lead to overlooking issues like chained assignment or incorrect `dtype` handling. Atlas addresses this by using its `read` tool to pull the full content of changed files, not just the isolated diff hunks. For example, if a change is made to a single line in `pandas/core/indexing.py`, Atlas will load the entire file, allowing you to see how that line interacts with preceding DataFrame constructions or subsequent method calls. This comprehensive view is essential for identifying potential `SettingWithCopyWarning` scenarios or understanding the full impact of a modification on a complex Pandas pipeline, ensuring the code behaves correctly under Copy-on-Write, which is the default in pandas 3. ## Identifying Pandas API Breakages with LSP in Atlas When reviewing a Pandas pull request, a changed function signature can silently break numerous callers across the codebase. Atlas employs its `lsp` tool's `findReferences` operation to proactively check all references for every modified signature, ensuring no more than 0 unexpected API breakages occur in your Pandas project. Pandas is a large and evolving library, and changes to function signatures can have far-reaching consequences that a simple diff won't reveal. Atlas mitigates this risk by integrating with Language Server Protocol (LSP) servers. For every changed function signature identified in a Pandas pull request,for instance, a modification to `DataFrame.merge` or `Series.apply`,Atlas automatically invokes the `lsp` tool's `findReferences` operation. This powerful capability scans the entire codebase, including files not touched by the current diff, to identify all callers of the modified signature. If a change to `pandas/core/frame.py` alters a method's parameters, Atlas will flag any `pandas/tests/` files or user code that now calls the method incorrectly, preventing runtime errors and ensuring API stability. ## Grepping for Stale Pandas Patterns Pandas codebases often accumulate old constant names, stale copies, or forgotten feature flags that a PR might miss. Atlas uses its `grep` tool to systematically search for these patterns across the entire repository, ensuring that all 10 instances of an outdated constant like `PANDAS_OLD_CONSTANT` are updated or removed. Maintaining consistency and removing technical debt is crucial in a large Pandas project. A pull request might introduce a new constant or refactor an existing feature, but fail to update all its occurrences. Atlas addresses this by leveraging its `grep` tool. After analyzing the diff, Atlas can be instructed to search for specific patterns that the change should have updated but did not. This includes old constant names (e.g., a deprecated `pd.options.mode.chained_assignment = 'warn'` that should be removed in pandas 3), stale copies of code, or feature flags that are no longer relevant. For example, if a PR introduces a new vectorized expression to replace a `df.apply` over rows, Atlas can `grep` for `df.apply(axis=1)` to ensure all instances are considered for optimization, helping to eliminate performance bottlenecks. ## Automated Pandas Test Execution and Reporting Ensuring the correctness of Pandas changes requires robust testing. Atlas integrates directly with your `pytest` test runner, executing tests that use `pandas.testing.assert_frame_equal` and reporting findings as a `todowrite` list. This ensures that all 10 new test cases added to `pandas/tests/` pass, validating DataFrame transformations and index handling. The ultimate validation for any Pandas pull request is its test suite. Atlas facilitates this by using its `bash` tool to execute your project's `pytest` tests. Specifically, it runs commands like `pytest pandas/tests/` to ensure that all changes, especially those involving DataFrame transformations, index handling, or merge keys, behave as expected. Atlas pays particular attention to tests that employ `pandas.testing.assert_frame_equal`, which is critical for verifying the equality of DataFrames and Series. After execution, Atlas collects the test results and reports any failures or warnings as a `todowrite` list, ordered by severity. This allows developers to quickly identify and address issues, ensuring that the Pandas codebase remains stable and reliable, especially with new features or bug fixes. ## Atlas's Safe and Transparent Review Workflow for Pandas Atlas prioritizes safety and transparency in every Pandas code review. It drafts a plan in a read-only plan agent and seeks approval before executing any changes, ensuring that no more than 0 unintended modifications are made. Every tool call is permission-gated, and all file edits are presented as a unified diff for explicit approval. Atlas is designed with a strong emphasis on safety and user control, which is paramount when making changes to a complex Pandas codebase. Before any modification is made, Atlas operates in a read-only plan agent, drafting a detailed plan of action. This plan is then presented to the user for explicit approval, ensuring full transparency. Every Atlas tool call, whether it's `bash` for running `pytest` or `grep` for searching patterns, is permission-gated against `allow`, `ask`, and `deny` rules. Furthermore, Atlas computes a unified diff for every proposed file edit and surfaces it for approval before writing, allowing you to review the exact changes to files like `pyproject.toml` or `pandas/core/frame.py`. This multi-layered approval process ensures that Atlas acts as a trusted assistant, never making unapproved changes to your Pandas project. ## Steps 1. Fetch the Pandas PR branch and generate a diff: Use Atlas's VCS layer to fetch the specific branch for the pull request. Then, instruct Atlas to use `bash` to produce the raw patch, showing changes across files like `pandas/core/frame.py` and `pandas/tests/series/test_indexing.py`. 2. Read full Pandas files for context: Employ Atlas's `read` tool to load the complete content of all changed Pandas files, not just the diff hunks. This provides crucial surrounding context to identify issues like chained assignment or `dtype` drift that a line-by-line diff might obscure, especially relevant for pandas 3's Copy-on-Write behavior. 3. Check for Pandas API breakages with LSP: For every modified function signature in the Pandas code, use Atlas's `lsp` tool with `findReferences`. This operation scans the entire codebase to ensure that no callers of methods like `DataFrame.apply` or `Series.merge` are broken by the proposed changes, even in files outside the diff. 4. Grep for stale Pandas patterns: Instruct Atlas to use its `grep` tool to search for outdated constant names, stale code copies, or feature flags that should have been updated. For example, `grep` for `df.apply(axis=1)` to identify potential vectorization opportunities or old `pd.options.mode.chained_assignment` settings. 5. Run Pandas tests and report findings: Execute the project's `pytest` suite using Atlas's `bash` tool, specifically focusing on tests that utilize `pandas.testing.assert_frame_equal`. Atlas will then report any test failures or warnings as a `todowrite` list, ordered by severity, highlighting issues in DataFrame transformations or index handling. 6. Format changed Pandas files with ruff: After review and potential fixes, use Atlas's `bash` tool to run `ruff format` on the modified Pandas files. This ensures consistent code style across the pull request, adhering to the project's formatting standards. ## FAQ ### How does Atlas help catch chained assignment bugs in Pandas PRs? Atlas helps catch chained assignment by reading the full content of changed Pandas files, not just diff hunks. This provides the necessary surrounding context to identify patterns that lead to `SettingWithCopyWarning` or incorrect behavior under pandas 3's Copy-on-Write default, which a line-by-line review might miss. ### Can Atlas run my existing `pytest` suite for Pandas code? Yes, Atlas uses its `bash` tool to execute your project's `pytest` suite, including tests that rely on `pandas.testing.assert_frame_equal`. It then collects and reports the findings as a `todowrite` list, allowing you to quickly address any failures in DataFrame transformations or index handling. ### How does Atlas ensure I don't break existing Pandas API calls? Atlas uses its `lsp` tool's `findReferences` operation for every changed function signature in a Pandas pull request. This scans the entire codebase to identify all callers, even in files outside the diff, ensuring that modifications to methods like `DataFrame.merge` do not introduce unexpected API breakages. ### Does Atlas support `ruff format` for Pandas code style? Yes, Atlas integrates with your existing Pandas toolchain. You can instruct Atlas to use its `bash` tool to run `ruff format` on any modified files, ensuring that your Pandas code adheres to consistent style guidelines across the pull request. ### How does Atlas handle security and permissions when reviewing Pandas code? Atlas prioritizes security with a permission-gated workflow. Every tool call is checked against `allow`, `ask`, or `deny` rules. It drafts plans in a read-only agent and presents a unified diff for every file edit, requiring explicit approval before any changes are written to your Pandas project. ### Can Atlas help optimize Pandas code by identifying `df.apply` over rows? Yes, Atlas can use its `grep` tool to search for patterns like `df.apply(axis=1)` within your Pandas codebase. This helps identify opportunities to replace inefficient row-wise operations with vectorized expressions, which can significantly improve performance, especially in large DataFrame transformations. --- Canonical HTML: https://runatlas.sh/resources/stacks/review-a-pull-request-in-pandas Source of truth: aeo_pages row `/resources/stacks/review-a-pull-request-in-pandas` (segment: Stacks) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.