⚡️ Speed up method IndexVariable.to_index by 64%

[codeflash-ai]

📄 64% (0.64x) speedup for IndexVariable.to_index in xarray/core/variable.py

⏱️ Runtime : 2.26 milliseconds → 1.38 milliseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 64% speedup by reducing unnecessary object creation in pandas Index operations, which are notoriously expensive in Python.

Key Optimizations:

1. Conditional MultiIndex name setting: The original code always created new level names for MultiIndex objects, even when all names were already valid. The optimization only calls set_names() when at least one name is None, avoiding expensive MultiIndex reconstruction when no changes are needed.

2. Conditional Index name setting: For regular Index objects, the optimization compares the current name with the desired name before calling set_names(). Since pandas Index objects are immutable, set_names() creates a new Index instance even when the name doesn't change. By skipping this when current_name == name, we eliminate unnecessary object creation.

Why This Matters:

Pandas Index operations involve significant overhead due to immutability guarantees and internal validation. Each set_names() call creates a new Index object with complete metadata copying. In xarray's coordinate system, IndexVariable objects are frequently created during dataset operations, making these micro-optimizations compound significantly.

The optimizations are particularly effective for workloads with:

• Datasets with many coordinate variables that already have proper names
• MultiIndex coordinates where level names are pre-defined
• Repeated index operations during data alignment and merging

These changes maintain full backward compatibility while reducing computational overhead in the common case where index names are already correctly set.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 210 Passed
🌀 Generated Regression Tests	✅ 2 Passed
⏪ Replay Tests	✅ 255 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	80.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_variable.py::TestIndexVariable.test_multiindex_default_level_names	26.3μs	27.5μs	-4.07%⚠️
test_variable.py::TestIndexVariable.test_to_index	13.1μs	13.5μs	-2.96%⚠️

🌀 Generated Regression Tests and Runtime

import numpy as np
import pandas as pd

# imports
import pytest
from xarray.core.variable import IndexVariable

# function to test: IndexVariable.to_index
# (see code provided in the prompt above)

# Basic Test Cases


def test_to_index_raises_on_ndim_not_1():
    # Test that IndexVariable only accepts 1D data
    arr = np.array([[1, 2], [3, 4]])
    with pytest.raises(ValueError):
        IndexVariable(("x", "y"), arr)

import numpy as np
import pandas as pd

# imports
import pytest
from xarray.core.variable import IndexVariable


# function to test (minimal, isolated implementation for testing)
class PandasIndexingAdapter:
    """Minimal adapter for testing; wraps a pandas.Index or array-like."""

    def __init__(self, array):
        if isinstance(array, pd.Index):
            self.array = array
        else:
            self.array = pd.Index(array)
        self.level = None  # Used for MultiIndex level selection


# unit tests

# ------------------ BASIC TEST CASES ------------------


def test_invalid_ndim_raises():
    # Should raise for non-1D
    with pytest.raises(ValueError):
        IndexVariable(("x", "y"), [[1, 2], [3, 4]])


# ------------------ LARGE SCALE TEST CASES ------------------

⏪ 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_to_index	1.67ms	1.01ms	64.1%✅

To edit these changes git checkout codeflash/optimize-IndexVariable.to_index-miiwx3kl and push.