⚡️ Speed up method TimeResampler.first_items by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for TimeResampler.first_items in xarray/core/groupby.py

⏱️ Runtime : 6.32 milliseconds → 5.95 milliseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 6% speedup through several targeted micro-optimizations in both _apply_loffset and first_items functions:

Key Optimizations in _apply_loffset:

• Reduced attribute access overhead: Caches result.index in a local variable idx to avoid repeated attribute lookups during condition checking
• Fast path for modern pandas: Attempts to use the internal _add_offset() method (available in pandas 2.2+) which is significantly faster than the standard + operator for DatetimeIndex operations. Falls back gracefully to the standard addition for compatibility
• Streamlined condition evaluation: Combines all offset validation checks into a single conditional block, reducing branching overhead

Key Optimizations in first_items:

• Reduced attribute access: Caches self.group_as_index in a local variable to avoid repeated attribute lookups
• Optimized Series construction: Pre-allocates NumPy arrays (values = np.arange(idx_size)) instead of creating them inline, reducing temporary object creation
• Categorical grouping optimization: Adds observed=True to groupby() which significantly improves performance when the grouper contains categorical data by avoiding unused category levels
• Efficient array conversion: Uses conditional logic to call counts.to_numpy() when available (pandas Series) vs np.asarray() for better memory efficiency

The line profiler shows the most significant gains come from the first_items method, particularly in the groupby operations where the observed=True parameter and reduced attribute access provide measurable performance improvements. The _apply_loffset optimizations are smaller but still meaningful for time-series resampling workflows where this function is called frequently.

These optimizations are particularly effective for workloads involving large time series data or categorical grouping operations, which are common use cases for xarray's resampling functionality.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	✅ 56 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	72.2%

⏪ Replay Tests and Runtime

To edit these changes git checkout codeflash/optimize-TimeResampler.first_items-mij2mvfm and push.