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@codeflash-ai codeflash-ai bot commented Nov 28, 2025

📄 122% (1.22x) speedup for IndexVariable._data_equals in xarray/core/variable.py

⏱️ Runtime : 8.54 milliseconds 3.84 milliseconds (best of 19 runs)

📝 Explanation and details

The optimization achieves a 122% speedup through two key improvements to the IndexVariable class:

1. Fast-path _data_equals method:

  • Original: Always calls _to_index() on both objects, which creates new pandas Index objects and handles name formatting
  • Optimized: Directly compares the underlying arrays via self._data.array.equals(other._data.array) first, falling back to the original logic only on exceptions
  • Why faster: Avoids the overhead of index creation and name processing for the common case where arrays can be compared directly

2. Conditional operations in _to_index:

  • Original: Always processes MultiIndex names using list comprehension and always calls set_names()
  • Optimized:
    • Uses any(name is None for name in names) to check if name processing is needed
    • Only creates new names when there are actually None values to replace
    • Uses tuple comprehension instead of list comprehension (slight memory efficiency)
    • Only calls set_names() when the name actually differs from self.name

Why these optimizations matter:

  • IndexVariable objects are frequently compared during xarray operations like merging, alignment, and coordinate handling
  • The _to_index() method is called whenever pandas Index objects need to be created, which happens during many coordinate operations
  • By avoiding unnecessary object creation and string formatting when the existing state is already correct, the code eliminates redundant work

Performance characteristics:

  • Best gains when comparing identical IndexVariable objects or when MultiIndex names are already properly set
  • Maintains full backward compatibility and error handling
  • The try/except pattern in _data_equals ensures robustness while optimizing the common path

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 255 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_xarrayteststest_concat_py_xarrayteststest_computation_py_xarrayteststest_formatting_py_xarray__replay_test_0.py::test_xarray_core_variable_IndexVariable__data_equals 5.42ms 2.14ms 153%✅

To edit these changes git checkout codeflash/optimize-IndexVariable._data_equals-miiuopnw and push.

Codeflash Static Badge

The optimization achieves a **122% speedup** through two key improvements to the `IndexVariable` class:

**1. Fast-path `_data_equals` method:**
- **Original**: Always calls `_to_index()` on both objects, which creates new pandas Index objects and handles name formatting
- **Optimized**: Directly compares the underlying arrays via `self._data.array.equals(other._data.array)` first, falling back to the original logic only on exceptions
- **Why faster**: Avoids the overhead of index creation and name processing for the common case where arrays can be compared directly

**2. Conditional operations in `_to_index`:**
- **Original**: Always processes MultiIndex names using list comprehension and always calls `set_names()`
- **Optimized**: 
  - Uses `any(name is None for name in names)` to check if name processing is needed
  - Only creates new names when there are actually `None` values to replace
  - Uses tuple comprehension instead of list comprehension (slight memory efficiency)
  - Only calls `set_names()` when the name actually differs from `self.name`

**Why these optimizations matter:**
- `IndexVariable` objects are frequently compared during xarray operations like merging, alignment, and coordinate handling
- The `_to_index()` method is called whenever pandas Index objects need to be created, which happens during many coordinate operations
- By avoiding unnecessary object creation and string formatting when the existing state is already correct, the code eliminates redundant work

**Performance characteristics:**
- Best gains when comparing identical IndexVariable objects or when MultiIndex names are already properly set
- Maintains full backward compatibility and error handling
- The try/except pattern in `_data_equals` ensures robustness while optimizing the common path
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 28, 2025 12:40
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Nov 28, 2025
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