PERF: MultiIndex.equals for equal length indexes by lukemanley · Pull Request #56990 · pandas-dev/pandas (original) (raw)
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- Tests added and passed if fixing a bug or adding a new feature
- All code checks passed.
- Added type annotations to new arguments/methods/functions.
- Added an entry in the latest
doc/source/whatsnew/v2.3.0.rstfile if fixing a bug or adding a new feature.
Avoids densifying the levels of the multiindex to check for equality.
from asv_bench.benchmarks.multiindex_object import Equals
b = Equals()
b.setup()
%timeit b.time_equals_deepcopy()
# 56.4 ms ± 1.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) -> main
# 3.77 ms ± 58.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) -> PR
PERF: MultiIndex.equals PERF: MultiIndex.equals for equal length indexes
| ) |
|---|
| if not np.array_equal(self_codes, new_codes): |
| return False |
| if not self_level[:0].equals(other_level[:0]): |
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Could we just use is_dtype_equal?
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The equality rules for numpy dtypes are not as strict as is_dtype_equal:
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: idx = pd.Index([1])
In [4]: idx.astype("int64").equals(idx.astype("int32"))
Out[4]: True
In [5]: pd.core.dtypes.common.is_dtype_equal(np.int64, np.int32)
Out[5]: False
pmhatre1 pushed a commit to pmhatre1/pandas-pmhatre1 that referenced this pull request
avoid densifying level values in MultiIndex.equals
whatsnew
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