⚡️ Speed up method TableIterator.get_result by 19%

[codeflash-ai]

📄 19% (0.19x) speedup for TableIterator.get_result in pandas/io/pytables.py

⏱️ Runtime : 15.4 microseconds → 13.0 microseconds (best of 20 runs)

📝 Explanation and details

The optimized code achieves an 18% speedup through several micro-optimizations focused on reducing attribute lookups and method call overhead:

Key Optimizations:

1. Local Variable Caching in get_result: The optimized version stores frequently accessed attributes (self.s, self.func, self.start, etc.) as local variables at the beginning of the method. This eliminates repeated attribute lookups throughout the function execution, which is particularly effective since Python attribute access has overhead.

2. Inlined Close Logic: Instead of calling self.close() which performs additional attribute lookups and method calls, the optimized version inlines the close logic directly in get_result. This avoids the function call overhead and reduces attribute access from self.auto_close and self.store.

3. Streamlined Chunksize Handling: The initialization logic for chunksize was simplified to avoid an unnecessary int() conversion when chunksize is None, using a more direct conditional assignment.

4. Optimized Filter Processing in read_coordinates: The method reduces repeated calculations by storing coords.min() and coords.max() in local variables, and optimizes the coordinate indexing operation.

Performance Impact by Test Case:

• Basic operations show 15-40% improvements, with the largest gains in simple table operations
• Edge cases with auto-close functionality benefit significantly (19.5% faster) due to the inlined close logic
• Large-scale operations maintain consistent 15-20% improvements, indicating the optimizations scale well

The optimizations are most effective for workloads with frequent get_result calls and moderate-sized datasets, where the reduced attribute lookup overhead compounds across many operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 26 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	76.9%

🌀 Generated Regression Tests and Runtime

import pytest
from pandas.io.pytables import TableIterator


# function to test
def get_result(data, start=None, stop=None, where=None, coordinates=False, chunksize=None, func=None):
    """
    Returns a selection from 'data' based on the given parameters.

    Parameters:
    - data: list, tuple, or other sequence type
    - start: int or None, start index (inclusive)
    - stop: int or None, stop index (exclusive)
    - where: list of bools or ints, or callable, or None. If bools, selects elements where True.
    - coordinates: bool, if True, 'where' must be a list of coordinates (ints or bools)
    - chunksize: int or None, if set, returns a generator yielding chunks of size 'chunksize'
    - func: callable or None, if set, applies func(start, stop, selection) before returning

    Returns:
    - list, generator, or func output
    """
    # Validate input type
    if not hasattr(data, '__getitem__') or not hasattr(data, '__len__'):
        raise TypeError("data must be a sequence type")

    n = len(data)
    # Default start/stop
    if start is None:
        start = 0
    if stop is None:
        stop = n
    if start < 0:
        start += n
    if stop < 0:
        stop += n
    if start < 0 or stop > n or start > stop:
        raise ValueError("Invalid start/stop range")

    # Select coordinates
    if coordinates:
        if where is None:
            raise ValueError("coordinates=True requires a 'where' argument")
        # where can be bool mask or indices
        if isinstance(where, (list, tuple)):
            if all(isinstance(x, bool) for x in where):
                if len(where) != stop - start:
                    raise ValueError("Length of bool mask does not match slice")
                selection = [i for i, b in enumerate(where, start) if b]
            elif all(isinstance(x, int) for x in where):
                if any(x < start or x >= stop for x in where):
                    raise ValueError("where must have index locations >= start and < stop")
                selection = list(where)
            else:
                raise TypeError("where must be a list of bools or ints when coordinates=True")
        else:
            raise TypeError("where must be a list or tuple when coordinates=True")
    else:
        # where can be None, bool mask, indices, or callable
        if where is None:
            selection = list(range(start, stop))
        elif callable(where):
            selection = [i for i in range(start, stop) if where(data[i])]
        elif isinstance(where, (list, tuple)):
            if all(isinstance(x, bool) for x in where):
                if len(where) != stop - start:
                    raise ValueError("Length of bool mask does not match slice")
                selection = [i for i, b in enumerate(where, start) if b]
            elif all(isinstance(x, int) for x in where):
                if any(x < start or x >= stop for x in where):
                    raise ValueError("where must have index locations >= start and < stop")
                selection = list(where)
            else:
                raise TypeError("where must be a list of bools or ints")
        else:
            raise TypeError("where must be None, callable, list, or tuple")

    # If chunksize is set, return generator
    def chunked():
        current = 0
        while current < len(selection):
            chunk = selection[current:current+chunksize]
            result = [data[i] for i in chunk]
            if func:
                result = func(None, None, chunk)
            yield result
            current += chunksize

    # Apply func if provided
    if func:
        result = func(start, stop, selection)
        return result

    # Return result
    if chunksize is not None:
        if not isinstance(chunksize, int) or chunksize <= 0:
            raise ValueError("chunksize must be a positive integer")
        return chunked()
    else:
        return [data[i] for i in selection]

# unit tests

# 1. Basic Test Cases

































#------------------------------------------------
import pytest
from pandas.io.pytables import TableIterator

# --- Function to test ---
# As the actual implementation of get_result is not provided, 
# we'll define a plausible version based on the docstring and code context above.
# The function seems to be a method of TableIterator, so we will define a minimal TableIterator class
# and a get_result method with the expected behavior for testing.

class DummyTable:
    """A dummy Table class to simulate Table behavior."""
    def __init__(self, nrows):
        self.nrows = nrows
        self.is_table = True
        self.read_coordinates_called = False
        self.read_coordinates_args = None

    def read_coordinates(self, where=None, start=None, stop=None):
        self.read_coordinates_called = True
        self.read_coordinates_args = (where, start, stop)
        # Simulate coordinates as a range of indices
        if start is None:
            start = 0
        if stop is None:
            stop = self.nrows
        return list(range(start, stop))

class DummyFixed:
    """A dummy Fixed class (not a Table)."""
    is_table = False

class DummyStore:
    """A dummy store to simulate HDFStore."""
    def __init__(self):
        self.closed = False
    def close(self):
        self.closed = True

def dummy_func(start, stop, where):
    """A dummy function to simulate the func callable."""
    # Just return a tuple of the inputs for testing
    return (start, stop, where)

# --- Unit tests for get_result ---

# 1. Basic Test Cases

def test_basic_table_coordinates_false():
    """Basic: Table, coordinates=False, should call func with where."""
    store = DummyStore()
    table = DummyTable(nrows=10)
    iterator = TableIterator(store, table, dummy_func, where=[1,2,3], nrows=10)
    codeflash_output = iterator.get_result(coordinates=False); result = codeflash_output # 1.93μs -> 1.37μs (40.9% faster)


def test_basic_fixed_coordinates_false():
    """Basic: Fixed, coordinates=False, should call func with where."""
    store = DummyStore()
    fixed = DummyFixed()
    iterator = TableIterator(store, fixed, dummy_func, where=[1,2], nrows=2)
    codeflash_output = iterator.get_result(coordinates=False); result = codeflash_output # 1.83μs -> 1.46μs (25.9% faster)

def test_basic_fixed_coordinates_true_raises():
    """Basic: Fixed, coordinates=True, should raise TypeError."""
    store = DummyStore()
    fixed = DummyFixed()
    iterator = TableIterator(store, fixed, dummy_func, where=[1], nrows=1)
    with pytest.raises(TypeError):
        iterator.get_result(coordinates=True) # 1.83μs -> 1.75μs (4.45% faster)


def test_basic_chunksize_fixed_raises():
    """Basic: Fixed, chunksize set, should raise TypeError."""
    store = DummyStore()
    fixed = DummyFixed()
    iterator = TableIterator(store, fixed, dummy_func, where=None, nrows=5, chunksize=2)
    with pytest.raises(TypeError):
        iterator.get_result() # 1.60μs -> 1.62μs (1.42% slower)

# 2. Edge Test Cases

def test_edge_empty_where_list():
    """Edge: where is empty list, should pass through to func."""
    store = DummyStore()
    table = DummyTable(nrows=0)
    iterator = TableIterator(store, table, dummy_func, where=[], nrows=0)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.37μs -> 1.06μs (29.6% faster)

def test_edge_none_where():
    """Edge: where is None, should pass through to func."""
    store = DummyStore()
    table = DummyTable(nrows=3)
    iterator = TableIterator(store, table, dummy_func, where=None, nrows=3)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.13μs -> 855ns (31.8% faster)


def test_edge_auto_close():
    """Edge: auto_close should close the store after get_result."""
    store = DummyStore()
    table = DummyTable(nrows=4)
    iterator = TableIterator(store, table, dummy_func, where=None, nrows=4, auto_close=True)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.88μs -> 1.57μs (19.5% faster)


def test_edge_func_returns_none():
    """Edge: func returns None, should return None."""
    def none_func(start, stop, where):
        return None
    store = DummyStore()
    table = DummyTable(nrows=2)
    iterator = TableIterator(store, table, none_func, where=[1], nrows=2)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.31μs -> 1.14μs (14.6% faster)

# 3. Large Scale Test Cases



def test_large_fixed():
    """Large: Fixed with large where list."""
    store = DummyStore()
    n = 1000
    fixed = DummyFixed()
    where = list(range(n))
    iterator = TableIterator(store, fixed, dummy_func, where=where, nrows=n)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.46μs -> 1.21μs (20.9% faster)

def test_large_empty_fixed():
    """Large: Fixed with empty where list."""
    store = DummyStore()
    fixed = DummyFixed()
    iterator = TableIterator(store, fixed, dummy_func, where=[], nrows=0)
    codeflash_output = iterator.get_result(); result = codeflash_output # 1.10μs -> 950ns (15.4% 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-TableIterator.get_result-mhc2x4qi and push.