Resolving Performance Bottlenecks in Pandas

About Pandas

Pandas is a fast, powerful, and widely used open-source Python library for data analysis and manipulation. It provides two core data structures (Series and DataFrame) that combine the ease of Python with high-performance operations implemented in optimized C and NumPy code. Pandas is foundational in the Python data ecosystem and is extensively used in data science, machine learning, finance, and scientific computing — making it the "Swiss Army knife" for structured data in Python.

Overview

This use case demonstrates how to use Artemis to identify complex technical debt in large-scale open-source repositories and automate the remediation process. Here, we diagnose and fix a critical performance regression in the pandas library itself.

1. Project Analysis & Issue Identification

We begin by onboarding a forked version of the pandas library. Rather than manually reviewing the codebase, we utilize the Artemis scanning engine to perform a targeted analysis. The workflow is comprised of the following components:

a) Target Repository

A fork of the pandas library, a high-volume codebase requiring strict adherence to performance standards.

b) Analysis Configuration

We configured the scanning engine to focus on three high-impact rule sets:

• Security Best Practices
• Error and Exception Handling
• Performance Optimization

c) Vulnerability Detection

The scan aggregated issues sorted by severity. The analysis detected a critical anomaly: "Critical performance bottleneck due to dense conversion for binary ufuncs." This issue flagged that operations on SparseArrays were inadvertently converting data to dense formats, causing unnecessary memory spikes and slowdowns.

Related GitHub Issue

d) Remediation Initiation

Upon selecting the issue, we utilized the sidebar interface to transition from detection to action by selecting "Plan and Fix".

2. Planning

2.1. Planning - Requirements Gathering

Once the "Plan and Fix" action was triggered, the Artemis planning agent initiated a context-aware session to scope the remediation. It analyzed the pandas/core/arrays/sparse/array.py file and the specific __array_ufunc__ method to formulate a strategy.

The agent utilized the Haiku thinking model to analyze the codebase and generate a set of targeted questions to clarify the implementation strategy. This phase ensured the fix would align with the library's architectural standards.

User & Technical Requirements Gathering

The planner engaged in a dialogue to refine the scope.

2.2. Plan Optimization & Reordering

A distinct capability of the planning agent was demonstrated during the "Thinking" phase. Initially, the agent generated a linear list of tasks. However, upon self-reflection, the agent reordered the tasks to prioritize testability.

• Initial Logic: Build helpers first, integrate last.
• Revised Logic: Integrate handlers first. This establishes the dispatcher structure immediately, allowing us to write integration tests right away even with stub handlers.

This reordering ensured that every subsequent step could be verified against a working entry point.

3. Plan Creation

Following the requirements gathering, the agent presented a finalized, dependency-aware plan containing 4 distinct subtasks.

The Strategy

The plan proposed replacing the dense conversion at lines 1763-1764 with a sparse-aware dispatcher.

The Task Sequence

1. Integrate handlers into __array_ufunc__ method: Establish the routing logic to detect binary ufunc conditions and dispatch accordingly.
2. Create helper function _sparse_ufunc_scalar: Handle the simplest case (SparseArray + Scalar) to make the dispatcher functional immediately.
3. Create handler for SparseArray + SparseArray ufuncs: Leverage existing _sparse_array_op infrastructure.
4. Create handler for SparseArray + dense array ufuncs: The most complex case, requiring dense-to-sparse conversion, scheduled last to build on previous patterns.

4. Task Execution

We proceeded to execute the plan sequentially. As per the reordered strategy, we ran the tasks one by one to ensure incremental stability.

Step 1: We initiated the "Integrate handlers" task. The agent generated the code to modify array.py, inserting the dispatch logic.

Step 2 & 3: We ran the scalar and sparse-sparse handler tasks.

Step 4: Finally, we executed the dense array handler task.

For each task, the interface provided a "Task Ready" view, allowing us to inspect the diffs before proceeding.

5. Code Review and PR Creation

Upon the completion of each task, the workflow transitioned to the review and publication stage.

Code Inspection

We reviewed the generated code changes in the Artemis interface to ensure they met the technical specifications.

Automated PR Generation

By clicking the Create Pull Request icon, Artemis triggered a generation workflow using Gemini 2.5 Flash.

Content Population

The AI automatically populated all required PR fields based on the context of the fix.

We successfully published the Pull Requests, completing the cycle from scanning a high-severity performance bottleneck to delivering a production-grade fix.

Performance Bottleneck Resolution

We successfully completed all 4 implementation tasks and merged the resulting Pull Requests into the pandas codebase. The cumulative changes eliminated the critical performance bottleneck by replacing the dense conversion in __array_ufunc__ with sparse-aware dispatching logic.

The fix ensures that binary ufuncs operate efficiently, only processing the non-zero elements of the SparseArray, leading to significant performance gains and reduced memory usage for sparse data operations.

Remediation Impact

Artemis' planning multi-agent system successfully identified a complex architectural flaw and orchestrated a surgical fix through informed task execution, adhering to best practices at every stage:

• Surgical Intervention: The fix targeted the specific lines of code in pandas/core/arrays/sparse/array.py, implementing a sparse-aware dispatcher rather than a broad, risky refactor.
• Dependency Management: The plan ensured the fix was leveraged and integrated with existing, well-tested code paths (_sparse_array_op), guaranteeing backward compatibility.
• Performance Expectation: The change enabled O(nnz) operations (complexity based on the number of non-zero elements) instead of O(n) dense allocation and computation, resulting in substantial speedup for large sparse arrays.

A full trace of the final commit hash, the test log, and performance benchmarks could be found below:

Repository

The complete fix was successfully contributed to the forked repository, ready for upstream submission.

Repository: https://github.com/RozhinKh/pandas.git

Commits:

• Baseline: feat(sparse): Integrate array_ufunc handlers for intelligent binary ufunc dispatch
• Final: feat(sparse): implement ufunc handler for SparseArray + dense array operations by converting dense operands to sparse to preserve sparsity