⚡️ Speed up function _postprocess_for_cut by 11%

[codeflash-ai]

📄 11% (0.11x) speedup for _postprocess_for_cut in pandas/core/reshape/tile.py

⏱️ Runtime : 49.3 microseconds → 44.3 microseconds (best of 346 runs)

📝 Explanation and details

The optimized code delivers an 11% speedup through two key optimizations:

1. Fast-path for ExtensionDtype in is_numeric_dtype
The original code always called _is_dtype_type first, then fell back to checking ExtensionDtype. The optimized version adds an early check for ExtensionDtype instances, directly returning arr_or_dtype._is_numeric without the expensive _is_dtype_type call. This eliminates unnecessary function overhead for ExtensionDtype inputs, which are common in pandas operations.

2. Reduced attribute access in _postprocess_for_cut
The original code accessed bins.dtype twice when bins was an Index - once for the is_numeric_dtype check and again implicitly. The optimized version caches bins.dtype in a local variable, eliminating the redundant attribute access. This micro-optimization reduces the overhead of Python's attribute lookup mechanism.

Performance Impact
The test results show consistent 6-24% improvements across various scenarios, with the largest gains occurring when:

• ExtensionDtype objects are frequently passed to is_numeric_dtype (21-24% faster)
• Index objects with numeric dtypes are processed in _postprocess_for_cut (14-24% faster)

These optimizations are particularly effective because they target the most common code paths - when bins are Index objects with numeric dtypes, which is typical in pandas binning operations. The improvements compound when these functions are called repeatedly in data processing workflows, making the optimizations especially valuable for performance-critical pandas operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 37 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	71.4%

🌀 Generated Regression Tests and Runtime

import pytest
from pandas.core.reshape.tile import _postprocess_for_cut


# Minimal Series and Index classes for testing
class Series:
    def __init__(self, data, index=None, name=None):
        self.data = list(data)
        self.index = list(index) if index is not None else list(range(len(data)))
        self.name = name
    def __eq__(self, other):
        return (
            isinstance(other, Series)
            and self.data == other.data
            and self.index == other.index
            and self.name == other.name
        )
    def __repr__(self):
        return f"Series(data={self.data}, index={self.index}, name={self.name})"

class Index:
    def __init__(self, data, dtype=None):
        self._values = list(data)
        self.dtype = dtype if dtype is not None else self._infer_dtype(data)
    def _infer_dtype(self, data):
        # crude dtype inference for test purposes
        if all(isinstance(x, int) for x in data):
            return "int64"
        elif all(isinstance(x, float) for x in data):
            return "float64"
        elif all(isinstance(x, str) for x in data):
            return "object"
        else:
            return "object"
    def __eq__(self, other):
        return (
            isinstance(other, Index)
            and self._values == other._values
            and self.dtype == other.dtype
        )
    def __repr__(self):
        return f"Index({self._values}, dtype={self.dtype})"

# 1. Basic Test Cases

def test_fac_returned_when_retbins_false_and_original_not_series():
    # fac should be returned unchanged if retbins is False and original is not a Series
    fac = ["a", "b", "c"]
    bins = [0, 1, 2]
    retbins = False
    original = [1, 2, 3]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.25μs -> 1.14μs (9.56% faster)

def test_fac_returned_as_series_when_original_is_series_and_retbins_false():
    # If original is a Series, fac should be reconstructed as a Series
    fac = ["a", "b", "c"]
    bins = [0, 1, 2]
    retbins = False
    original = Series([1, 2, 3], index=["x", "y", "z"], name="foo")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.34μs -> 1.10μs (21.6% faster)

def test_return_tuple_when_retbins_true_and_bins_not_index():
    # If retbins is True and bins is not Index, return (fac, bins)
    fac = ["a", "b", "c"]
    bins = [0, 1, 2]
    retbins = True
    original = [1, 2, 3]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.28μs -> 1.21μs (6.04% faster)

def test_return_tuple_with_series_when_retbins_true_and_original_is_series():
    # If original is Series and retbins is True, return (Series, bins)
    fac = ["a", "b", "c"]
    bins = [0, 1, 2]
    retbins = True
    original = Series([1, 2, 3], index=["x", "y", "z"], name="foo")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.30μs -> 1.14μs (13.7% faster)

def test_bins_index_numeric_dtype_returns_values():
    # If bins is Index with numeric dtype and retbins is True, bins should be replaced with ._values
    fac = ["a", "b", "c"]
    bins = Index([0, 1, 2], dtype="int64")
    retbins = True
    original = [1, 2, 3]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.41μs -> 1.14μs (23.9% faster)

def test_bins_index_non_numeric_dtype_returns_index():
    # If bins is Index with non-numeric dtype, bins should not be replaced
    fac = ["a", "b", "c"]
    bins = Index(["a", "b", "c"], dtype="object")
    retbins = True
    original = [1, 2, 3]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.39μs -> 1.11μs (24.4% faster)

# 2. Edge Test Cases

def test_empty_fac_and_bins():
    # Test with empty fac and bins
    fac = []
    bins = []
    retbins = True
    original = []
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.24μs -> 1.14μs (8.05% faster)

def test_none_fac_and_bins():
    # Test with None fac and bins
    fac = None
    bins = None
    retbins = True
    original = None
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.25μs -> 1.18μs (6.30% faster)

def test_original_is_series_with_empty_data():
    # Series with empty data should be reconstructed correctly
    fac = []
    bins = [0, 1]
    retbins = False
    original = Series([], index=[], name="empty")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.17μs -> 1.07μs (9.29% faster)

def test_bins_index_with_float_dtype():
    # Index with float dtype should be replaced with ._values
    fac = ["a", "b"]
    bins = Index([0.0, 1.0], dtype="float64")
    retbins = True
    original = [1, 2]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.33μs -> 1.17μs (14.2% faster)

def test_original_is_series_and_bins_is_index():
    # Both original is Series and bins is Index with numeric dtype
    fac = ["a", "b"]
    bins = Index([10, 20], dtype="int64")
    retbins = True
    original = Series([1, 2], index=["i", "j"], name="bar")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.38μs -> 1.17μs (17.8% faster)

def test_bins_index_with_mixed_dtype():
    # Index with mixed dtype should not be replaced
    fac = ["a", "b"]
    bins = Index([0, "b"], dtype="object")
    retbins = True
    original = [1, 2]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.38μs -> 1.14μs (21.0% faster)

def test_original_is_series_and_retbins_false_and_bins_index():
    # original is Series, retbins is False, bins is Index
    fac = ["a", "b"]
    bins = Index([0, 1], dtype="int64")
    retbins = False
    original = Series([1, 2], index=["x", "y"], name="baz")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.20μs -> 1.06μs (12.9% faster)

def test_original_is_not_series_and_bins_is_index():
    # original is not Series, bins is Index, retbins True
    fac = ["a", "b"]
    bins = Index([0, 1], dtype="int64")
    retbins = True
    original = [1, 2]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.30μs -> 1.18μs (10.2% faster)

def test_original_is_series_and_bins_is_index_non_numeric():
    # original is Series, bins is Index non-numeric
    fac = ["a", "b"]
    bins = Index(["a", "b"], dtype="object")
    retbins = True
    original = Series([1, 2], index=["u", "v"], name="qux")
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.42μs -> 1.19μs (19.2% faster)

# 3. Large Scale Test Cases

def test_large_fac_and_bins():
    # Large fac and bins, retbins True
    fac = ["a"] * 1000
    bins = list(range(1000))
    retbins = True
    original = [1] * 1000
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.32μs -> 1.21μs (9.02% faster)

def test_large_fac_and_bins_index_numeric():
    # Large fac, bins is Index with numeric dtype
    fac = [str(i) for i in range(1000)]
    bins = Index(range(1000), dtype="int64")
    retbins = True
    original = [i for i in range(1000)]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.43μs -> 1.28μs (11.5% faster)

def test_large_series_reconstruction():
    # Large fac, original is Series, retbins False
    fac = [str(i) for i in range(1000)]
    idx = [f"idx{i}" for i in range(1000)]
    original = Series([i for i in range(1000)], index=idx, name="bigseries")
    retbins = False
    bins = [i for i in range(1000)]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.28μs -> 1.23μs (4.15% faster)

def test_large_series_and_bins_index_numeric():
    # Large fac, original is Series, bins is Index with numeric dtype, retbins True
    fac = [str(i) for i in range(1000)]
    idx = [f"idx{i}" for i in range(1000)]
    original = Series([i for i in range(1000)], index=idx, name="bigseries")
    bins = Index(range(1000), dtype="int64")
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.50μs -> 1.30μs (14.6% faster)

def test_large_bins_index_non_numeric():
    # Large bins Index with non-numeric dtype
    fac = [str(i) for i in range(1000)]
    bins = Index([str(i) for i in range(1000)], dtype="object")
    retbins = True
    original = [i for i in range(1000)]
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.45μs -> 1.36μs (6.61% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from collections import namedtuple

# imports
import pytest
from pandas.core.reshape.tile import _postprocess_for_cut

# --- Minimal stubs to allow testing without pandas ---

# Minimal Index stub
class Index(list):
    def __init__(self, data, dtype=None):
        super().__init__(data)
        self.dtype = dtype
        self._values = list(data)

# Minimal ABCSeries stub
class ABCSeries:
    pass

# Minimal Series stub (inherits from ABCSeries)
class Series(ABCSeries):
    def __init__(self, data, index=None, name=None):
        self.data = list(data)
        self.index = index if index is not None else list(range(len(data)))
        self.name = name
    def __eq__(self, other):
        # For testing: compare data, index, and name
        if not isinstance(other, Series):
            return False
        return self.data == other.data and self.index == other.index and self.name == other.name
    # Simulate pandas' _constructor property
    @property
    def _constructor(self):
        return Series
from pandas.core.reshape.tile import _postprocess_for_cut

# --- Unit tests ---

# 1. Basic Test Cases

def test_basic_fac_and_bins_with_series_and_retbins_true():
    # fac is a list, bins is Index of ints, original is Series, retbins True
    fac = ['a', 'b', 'c']
    bins = Index([1, 2, 3, 4], dtype='int64')
    original = Series([10, 20, 30], index=['x', 'y', 'z'], name='foo')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.65μs -> 1.60μs (2.93% faster)
    fac_out, bins_out = result

def test_basic_fac_and_bins_with_series_and_retbins_false():
    # retbins False: should return just fac (as Series)
    fac = ['a', 'b']
    bins = Index([0, 1, 2], dtype='int64')
    original = Series([1, 2], index=[100, 200], name='bar')
    retbins = False
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.28μs -> 1.15μs (11.8% faster)

def test_basic_fac_and_bins_with_nonseries_and_retbins_true():
    # original is not Series: fac is a list, bins is Index, retbins True
    fac = [0, 1]
    bins = Index([5, 6, 7], dtype='int64')
    original = [42, 43]  # not a Series
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.33μs -> 1.20μs (10.6% faster)
    fac_out, bins_out = result

def test_basic_fac_and_bins_with_nonseries_and_retbins_false():
    # original is not Series: fac is a list, bins is Index, retbins False
    fac = [0, 1]
    bins = Index([5, 6, 7], dtype='int64')
    original = None
    retbins = False
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.12μs -> 1.03μs (8.66% faster)

# 2. Edge Test Cases

def test_bins_not_numeric_dtype_index():
    # bins is Index but not numeric dtype, retbins True
    fac = ['x', 'y']
    bins = Index(['a', 'b', 'c'], dtype='object')
    original = Series([1, 2], index=[0, 1], name='baz')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.31μs -> 1.17μs (12.5% faster)
    fac_out, bins_out = result

def test_bins_not_index_type():
    # bins is a list, not Index
    fac = [1, 2]
    bins = [0.0, 1.0, 2.0]
    original = Series([1, 2], name='qux')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.26μs -> 1.15μs (10.4% faster)
    fac_out, bins_out = result

def test_original_is_none():
    # original is None, should not wrap fac
    fac = [1, 2, 3]
    bins = Index([0, 1, 2, 3], dtype='int64')
    original = None
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.32μs -> 1.16μs (13.5% faster)
    fac_out, bins_out = result

def test_bins_index_without_dtype():
    # bins is Index with no dtype attribute
    class DummyIndex(list):
        pass
    bins = DummyIndex([1, 2, 3])
    fac = [0, 1]
    original = [1, 2]
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.53μs -> 1.37μs (11.8% faster)
    fac_out, bins_out = result

def test_fac_is_empty():
    # fac is empty, bins is Index, original is Series
    fac = []
    bins = Index([1, 2], dtype='int64')
    original = Series([], index=[], name='empty')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.31μs -> 1.14μs (15.2% faster)
    fac_out, bins_out = result

def test_bins_is_none():
    # bins is None
    fac = [1, 2]
    bins = None
    original = Series([1, 2])
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.28μs -> 1.18μs (8.38% faster)
    fac_out, bins_out = result

def test_fac_is_series_already():
    # fac is already a Series, original is Series
    fac = Series([10, 20], index=[1, 2], name='foo')
    bins = Index([0, 1, 2], dtype='int64')
    original = Series([30, 40], index=[1, 2], name='foo')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.27μs -> 1.19μs (6.90% faster)
    fac_out, bins_out = result

def test_original_is_object_with_constructor():
    # original is not Series but has _constructor attribute
    class Dummy:
        def __init__(self):
            self._constructor = lambda fac, index=None, name=None: 'wrapped'
            self.index = [0, 1]
            self.name = 'dummy'
    fac = [1, 2]
    bins = Index([0, 1, 2], dtype='int64')
    original = Dummy()
    retbins = True
    # Should not wrap, as Dummy is not an ABCSeries
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.41μs -> 1.31μs (7.33% faster)
    fac_out, bins_out = result

def test_bins_is_index_with_non_numeric_dtype():
    # bins is Index with dtype 'bool'
    fac = [0, 1]
    bins = Index([True, False, True], dtype='bool')
    original = Series([1, 2], name='bools')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.29μs -> 1.16μs (10.7% faster)
    fac_out, bins_out = result

# 3. Large Scale Test Cases

def test_large_fac_and_bins_series():
    # fac and bins are large, original is Series
    N = 1000
    fac = [i % 10 for i in range(N)]
    bins = Index(list(range(N + 1)), dtype='int64')
    original = Series(list(range(N)), index=list(range(N)), name='large')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.39μs -> 1.26μs (9.83% faster)
    fac_out, bins_out = result

def test_large_fac_and_bins_nonseries():
    # fac and bins are large, original is not Series
    N = 1000
    fac = [str(i % 5) for i in range(N)]
    bins = Index([float(i) for i in range(N + 1)], dtype='float64')
    original = None
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.36μs -> 1.26μs (7.78% faster)
    fac_out, bins_out = result

def test_large_fac_and_bins_with_non_numeric_dtype():
    # bins is Index of strings, should not convert to list
    N = 1000
    fac = [i for i in range(N)]
    bins = Index([str(i) for i in range(N + 1)], dtype='object')
    original = Series([i for i in range(N)], name='strbins')
    retbins = True
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.39μs -> 1.28μs (7.86% faster)
    fac_out, bins_out = result

def test_large_fac_and_bins_retbins_false():
    # Large fac, retbins False
    N = 1000
    fac = [i for i in range(N)]
    bins = Index([i for i in range(N + 1)], dtype='int64')
    original = Series([i for i in range(N)], name='nofalse')
    retbins = False
    codeflash_output = _postprocess_for_cut(fac, bins, retbins, original); result = codeflash_output # 1.24μs -> 1.19μs (4.46% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-_postprocess_for_cut-mhoaqf09 and push.