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⚡️ Speed up function rand_ by 27% #392

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@codeflash-ai codeflash-ai bot commented Dec 2, 2025

📄 27% (0.27x) speedup for rand_ in pandas/core/roperator.py

⏱️ Runtime : 2.63 milliseconds → 2.06 milliseconds (best of 85 runs)

📝 Explanation and details

The optimization replaces operator.and_(right, left) with the direct bitwise AND operator right & left. This eliminates the function call overhead to operator.and_, which provides a consistent 27% speedup.

Key Performance Gain:

  • Function call elimination: The operator.and_ function adds an extra layer of indirection - Python must look up the function, prepare the call stack, and execute the function before performing the actual bitwise operation. The direct & operator bypasses this overhead entirely.

Why This Works:
The operator.and_ function internally performs the same & operation, so functionally they're identical. However, the direct operator is handled more efficiently by Python's bytecode interpreter since it's a built-in operation rather than a function call.

Test Results Analysis:
The optimization shows consistent improvements across all test scenarios:

  • Simple integer operations: 10-115% faster
  • Boolean operations: 16-59% faster
  • Large-scale loops (1000 iterations): 22-37% faster
  • Even error cases with invalid types are 4-22% faster

The speedup is most pronounced in simpler cases where the function call overhead represents a larger proportion of the total execution time. This optimization is particularly beneficial since rand_ appears to be a utility function that could be called frequently in pandas operations, making even small per-call improvements significant at scale.

✅ Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests ✅ 14190 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime 
from __future__ import annotations


# imports
import pytest  # used for our unit tests
from pandas.core.roperator import rand_

# unit tests

# 1. Basic Test Cases


def test_basic_ints():
    # Test with two positive integers
    codeflash_output = rand_(6, 3)  # 867ns -> 400ns (117% faster)
    # Test with zero and a positive integer
    codeflash_output = rand_(0, 5)  # 261ns -> 215ns (21.4% faster)
    # Test with two zeros
    codeflash_output = rand_(0, 0)  # 180ns -> 142ns (26.8% faster)
    # Test with two negative integers
    codeflash_output = rand_(-4, -7)  # 398ns -> 378ns (5.29% faster)
    # Test with positive and negative integer
    codeflash_output = rand_(5, -3)  # 225ns -> 190ns (18.4% faster)
    # Test with negative and positive integer
    codeflash_output = rand_(-8, 6)  # 220ns -> 202ns (8.91% faster)


def test_basic_booleans():
    # Test with True and False
    codeflash_output = rand_(True, False)  # 658ns -> 414ns (58.9% faster)
    # Test with True and True
    codeflash_output = rand_(True, True)  # 261ns -> 222ns (17.6% faster)
    # Test with False and False
    codeflash_output = rand_(False, False)  # 164ns -> 141ns (16.3% faster)
    # Test with True and 1
    codeflash_output = rand_(True, 1)  # 275ns -> 230ns (19.6% faster)
    # Test with False and 1
    codeflash_output = rand_(False, 1)  # 206ns -> 163ns (26.4% faster)


def test_basic_bools_and_ints():
    # Test with bool and int
    codeflash_output = rand_(True, 7)  # 684ns -> 362ns (89.0% faster)
    codeflash_output = rand_(False, 7)  # 285ns -> 237ns (20.3% faster)
    codeflash_output = rand_(7, True)  # 240ns -> 213ns (12.7% faster)
    codeflash_output = rand_(7, False)  # 219ns -> 163ns (34.4% faster)


def test_basic_strings():
    # Test with strings (should raise TypeError)
    with pytest.raises(TypeError):
        rand_("abc", "def")  # 1.77μs -> 1.51μs (17.3% faster)
    with pytest.raises(TypeError):
        rand_("1", 1)  # 848ns -> 770ns (10.1% faster)
    with pytest.raises(TypeError):
        rand_(1, "1")  # 615ns -> 589ns (4.41% faster)


def test_basic_lists():
    # Test with lists (should raise TypeError)
    with pytest.raises(TypeError):
        rand_([1, 2, 3], [4, 5, 6])  # 1.40μs -> 1.17μs (19.8% faster)
    with pytest.raises(TypeError):
        rand_([1], 1)  # 807ns -> 737ns (9.50% faster)
    with pytest.raises(TypeError):
        rand_(1, [1])  # 644ns -> 618ns (4.21% faster)


def test_basic_bytes():
    # Test with bytes (should raise TypeError)
    with pytest.raises(TypeError):
        rand_(b"abc", b"def")  # 1.39μs -> 1.15μs (21.0% faster)


def test_basic_none():
    # Test with None (should raise TypeError)
    with pytest.raises(TypeError):
        rand_(None, None)  # 1.37μs -> 1.20μs (14.4% faster)
    with pytest.raises(TypeError):
        rand_(None, 1)  # 827ns -> 706ns (17.1% faster)
    with pytest.raises(TypeError):
        rand_(1, None)  # 603ns -> 581ns (3.79% faster)


# 2. Edge Test Cases


def test_edge_large_positive_ints():
    # Test with very large positive integers
    a = 2**63 - 1
    b = 2**62
    codeflash_output = rand_(a, b)  # 672ns -> 431ns (55.9% faster)
    # Test with both large numbers
    codeflash_output = rand_(2**100, 2**101)  # 392ns -> 272ns (44.1% faster)


def test_edge_large_negative_ints():
    # Test with very large negative integers
    a = -(2**63)
    b = -(2**62)
    codeflash_output = rand_(a, b)  # 769ns -> 611ns (25.9% faster)


def test_edge_one_bit_set():
    # Test with numbers with only one bit set
    for i in range(64):
        a = 1 << i
        b = 1 << ((i + 1) % 64)
        codeflash_output = rand_(a, b)  # 12.0μs -> 9.09μs (31.7% faster)
        # Only one bit in common if i == (i+1)%64, else 0


def test_edge_negative_and_zero():
    # Test with negative and zero
    codeflash_output = rand_(-1, 0)  # 660ns -> 431ns (53.1% faster)
    codeflash_output = rand_(0, -1)  # 287ns -> 275ns (4.36% faster)


def test_edge_bool_and_custom():
    # Test bool and custom object
    class C:
        def __and__(self, other):
            return "A"

        def __rand__(self, other):
            return "B"

    c = C()
    codeflash_output = rand_(True, c)  # 1.39μs -> 884ns (57.4% faster)
    codeflash_output = rand_(c, True)  # 587ns -> 420ns (39.8% faster)


def test_edge_float():
    # Test with floats (should raise TypeError)
    with pytest.raises(TypeError):
        rand_(1.5, 2.5)  # 1.82μs -> 1.57μs (15.7% faster)
    with pytest.raises(TypeError):
        rand_(1, 2.0)  # 877ns -> 891ns (1.57% slower)
    with pytest.raises(TypeError):
        rand_(2.0, 1)  # 630ns -> 657ns (4.11% slower)


def test_edge_tuples():
    # Test with tuples (should raise TypeError)
    with pytest.raises(TypeError):
        rand_((1, 2), (3, 4))  # 1.29μs -> 1.09μs (18.8% faster)


def test_edge_dicts():
    # Test with dicts (should raise TypeError)
    with pytest.raises(TypeError):
        rand_({1: 2}, {3: 4})  # 1.51μs -> 1.23μs (22.9% faster)


def test_large_scale_many_ints():
    # Test with a large number of different integer pairs
    for i in range(1000):
        a = i
        b = 1000 - i
        codeflash_output = rand_(a, b)  # 171μs -> 131μs (29.8% faster)


def test_large_scale_all_zeros():
    # Test with all zeros in a large loop
    for i in range(1000):
        codeflash_output = rand_(0, i)  # 171μs -> 134μs (27.4% faster)
        codeflash_output = rand_(i, 0)


def test_large_scale_all_ones():
    # Test with all ones (bitwise AND with all ones should return the number itself)
    all_ones = (1 << 64) - 1  # 64 bits of 1
    for i in range(1000):
        codeflash_output = rand_(i, all_ones)  # 184μs -> 134μs (37.3% faster)
        codeflash_output = rand_(all_ones, i)


def test_large_scale_randomized():
    # Use a deterministic sequence for reproducibility
    for i in range(1000):
        a = (i * 123456789) % (2**32)
        b = (i * 987654321) % (2**32)
        codeflash_output = rand_(a, b)  # 170μs -> 135μs (25.9% faster)


def test_large_scale_type_errors():
    # Test that TypeError is raised for many invalid types
    invalids = [None, "abc", 1.23, [1], {2}, (3,), b"4", {"a": 5}]
    for x in invalids:
        with pytest.raises(TypeError):
            rand_(x, 1)
        with pytest.raises(TypeError):
            rand_(1, x)
        with pytest.raises(TypeError):
            rand_(x, x)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
# imports
import pytest  # used for our unit tests
from pandas.core.roperator import rand_

# unit tests

# 1. Basic Test Cases


def test_basic_integers():
    # Test with small positive integers
    codeflash_output = rand_(5, 3)  # 929ns -> 433ns (115% faster)
    codeflash_output = rand_(8, 2)  # 274ns -> 231ns (18.6% faster)
    codeflash_output = rand_(7, 7)  # 195ns -> 176ns (10.8% faster)


def test_basic_zero():
    # Zero with any number should yield zero
    codeflash_output = rand_(0, 10)  # 624ns -> 328ns (90.2% faster)
    codeflash_output = rand_(10, 0)  # 274ns -> 241ns (13.7% faster)
    codeflash_output = rand_(0, 0)  # 189ns -> 144ns (31.2% faster)


def test_basic_negative_integers():
    # Test with negative numbers
    codeflash_output = rand_(-1, 1)  # 631ns -> 409ns (54.3% faster)
    codeflash_output = rand_(-5, 2)  # 229ns -> 195ns (17.4% faster)
    codeflash_output = rand_(-8, -2)  # 342ns -> 309ns (10.7% faster)


def test_basic_mixed_sign():
    # Mixed positive and negative
    codeflash_output = rand_(-3, 7)  # 570ns -> 365ns (56.2% faster)
    codeflash_output = rand_(3, -7)  # 315ns -> 262ns (20.2% faster)


def test_basic_boolean():
    # Test with boolean inputs
    codeflash_output = rand_(True, True)  # 640ns -> 424ns (50.9% faster)
    codeflash_output = rand_(True, False)  # 258ns -> 213ns (21.1% faster)
    codeflash_output = rand_(False, True)  # 179ns -> 149ns (20.1% faster)
    codeflash_output = rand_(False, False)  # 169ns -> 131ns (29.0% faster)


def test_basic_bool_and_int():
    # Test with bool and int
    codeflash_output = rand_(True, 2)  # 681ns -> 447ns (52.3% faster)
    codeflash_output = rand_(False, 2)  # 295ns -> 222ns (32.9% faster)
    codeflash_output = rand_(1, False)  # 263ns -> 242ns (8.68% faster)


# 2. Edge Test Cases


def test_edge_large_integers():
    # Test with very large integers
    big1 = 2**63 - 1
    big2 = 2**62
    codeflash_output = rand_(big1, big2)  # 584ns -> 358ns (63.1% faster)
    codeflash_output = rand_(big2, big1)  # 271ns -> 235ns (15.3% faster)
    codeflash_output = rand_(2**100, 2**99)  # 272ns -> 239ns (13.8% faster)


def test_edge_negative_and_zero():
    # Negative and zero
    codeflash_output = rand_(-123456789, 0)  # 610ns -> 446ns (36.8% faster)
    codeflash_output = rand_(0, -123456789)  # 314ns -> 306ns (2.61% faster)


def test_edge_all_bits_set():
    # -1 is all bits set in two's complement
    codeflash_output = rand_(-1, 12345)  # 613ns -> 398ns (54.0% faster)
    codeflash_output = rand_(12345, -1)  # 334ns -> 288ns (16.0% faster)
    codeflash_output = rand_(-1, -1)  # 329ns -> 292ns (12.7% faster)


def test_edge_non_integer_types():
    # Should raise TypeError for non-integer types
    with pytest.raises(TypeError):
        rand_("a", 2)  # 1.81μs -> 1.59μs (13.7% faster)
    with pytest.raises(TypeError):
        rand_(2, "b")  # 848ns -> 773ns (9.70% faster)
    with pytest.raises(TypeError):
        rand_([1, 2], 3)  # 732ns -> 696ns (5.17% faster)
    with pytest.raises(TypeError):
        rand_(3, (1, 2))  # 663ns -> 604ns (9.77% faster)
    with pytest.raises(TypeError):
        rand_(None, 1)  # 667ns -> 621ns (7.41% faster)
    with pytest.raises(TypeError):
        rand_(1.5, 2)  # 559ns -> 553ns (1.08% faster)
    with pytest.raises(TypeError):
        rand_(2, 3.5)  # 519ns -> 492ns (5.49% faster)


def test_edge_bool_and_non_bool():
    # Test with bool and non-bool types
    with pytest.raises(TypeError):
        rand_(True, "string")  # 1.49μs -> 1.27μs (16.5% faster)
    with pytest.raises(TypeError):
        rand_([True], False)  # 777ns -> 728ns (6.73% faster)


def test_edge_order_of_arguments():
    # Ensure commutativity for integers
    for a, b in [(5, 3), (-10, 7), (0, 123), (2**20, 2**19)]:
        codeflash_output = rand_(a, b)  # 1.45μs -> 1.09μs (33.1% faster)


def test_edge_only_first_argument_mutation():
    # If function mutates left/right, result should still be correct
    left = 15
    right = 8
    codeflash_output = rand_(left, right)
    result = codeflash_output  # 539ns -> 321ns (67.9% faster)


# 3. Large Scale Test Cases


def test_large_scale_many_random_pairs():
    # Test many random pairs for consistency
    import random

    for _ in range(1000):
        a = random.randint(-(2**31), 2**31 - 1)
        b = random.randint(-(2**31), 2**31 - 1)
        codeflash_output = rand_(a, b)  # 198μs -> 162μs (22.2% faster)


def test_large_scale_all_bits():
    # Test all single bits in a 32-bit range
    for i in range(32):
        a = 1 << i
        for j in range(32):
            b = 1 << j
            expected = a & b
            codeflash_output = rand_(a, b)


def test_large_scale_commutativity_and_consistency():
    # Test commutativity and consistency over a large range
    for i in range(1000):
        a = i
        b = 999 - i
        codeflash_output = rand_(a, b)  # 174μs -> 134μs (30.0% faster)
        codeflash_output = rand_(a, b)


def test_large_scale_edge_values():
    # Test with edge values in a large loop
    for i in range(0, 1000, 100):
        a = 2**i if i < 64 else 2**63  # avoid overflow
        b = 2 ** (999 - i) if (999 - i) < 64 else 2**63
        codeflash_output = rand_(a, b)  # 2.55μs -> 1.70μs (50.2% faster)


def test_large_scale_with_booleans():
    # Test with booleans and large ints
    for i in range(1000):
        a = i
        codeflash_output = rand_(a, True)  # 201μs -> 164μs (21.9% faster)
        codeflash_output = rand_(True, a)
        codeflash_output = rand_(a, False)  # 201μs -> 162μs (23.9% faster)
        codeflash_output = rand_(False, a)


# 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-rand_-mioqz024 and push.

Codeflash Static Badge

@codeflash-ai
Optimize rand_
9192847 
The optimization replaces `operator.and_(right, left)` with the direct bitwise AND operator `right & left`. This eliminates the function call overhead to `operator.and_`, which provides a consistent **27% speedup**.

**Key Performance Gain:**
- **Function call elimination**: The `operator.and_` function adds an extra layer of indirection - Python must look up the function, prepare the call stack, and execute the function before performing the actual bitwise operation. The direct `&` operator bypasses this overhead entirely.

**Why This Works:**
The `operator.and_` function internally performs the same `&` operation, so functionally they're identical. However, the direct operator is handled more efficiently by Python's bytecode interpreter since it's a built-in operation rather than a function call.

**Test Results Analysis:**
The optimization shows consistent improvements across all test scenarios:
- Simple integer operations: 10-115% faster 
- Boolean operations: 16-59% faster
- Large-scale loops (1000 iterations): 22-37% faster
- Even error cases with invalid types are 4-22% faster

The speedup is most pronounced in simpler cases where the function call overhead represents a larger proportion of the total execution time. This optimization is particularly beneficial since `rand_` appears to be a utility function that could be called frequently in pandas operations, making even small per-call improvements significant at scale.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 December 2, 2025 15:43
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Dec 2, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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