⚡️ Speed up method TableIterator.get_result by 16%

[codeflash-ai]

📄 16% (0.16x) speedup for TableIterator.get_result in pandas/io/pytables.py

⏱️ Runtime : 26.7 microseconds → 23.0 microseconds (best of 14 runs)

📝 Explanation and details

The optimization achieves a 15% speedup by reducing attribute lookups and method call overhead in the hot path of get_result().

Key optimizations:

1. Attribute lookup hoisting: Store self.s, self.chunksize, self.func, self.start, and self.stop in local variables to avoid repeated attribute access overhead. The line profiler shows this saves significant time per hit.

2. Inline auto_close logic: Replace the self.close() method call with direct inline check if self.auto_close: self.store.close(), eliminating method call overhead.

3. Optimized initialization logic: Cache self.s.is_table lookup and use conditional expressions instead of multiple if/else branches for setting default values.

4. Reduced filter processing overhead: In read_coordinates(), precompute coords.min() and coords.max() + 1 once instead of recalculating in the loop.

Performance impact by test case:

• Basic cases show 25-37% improvements, indicating the optimizations are most effective for simple, frequent operations
• Error cases show minimal impact (1-11%), which is expected since exception raising dominates
• Large-scale operations show 9-28% improvements, demonstrating the optimizations scale well

The optimizations target Python's attribute access overhead, which becomes significant in frequently called methods like get_result(). Since this is part of pandas' HDF5 I/O system, these micro-optimizations can compound to meaningful performance gains in data processing pipelines that repeatedly read table chunks or coordinates.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 42 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

--- Minimal stubs for dependencies ---

These are minimal, deterministic stubs to allow TableIterator.get_result to be tested in isolation.

They are NOT mocks; they implement only the minimal functional interface needed for the tests.

class DummyStore:
def init(self):
self.closed = False
def close(self):
self.closed = True

class DummyFixed:
is_table = False

class DummyTable:
is_table = True
def init(self, coords=None):
self._coords = coords if coords is not None else list(range(10))
def read_coordinates(self, where=None, start=None, stop=None):
# Simulate coordinate selection, ignoring 'where' for simplicity
# If start/stop are provided, slice accordingly
coords = self._coords
if start is not None or stop is not None:
start = start if start is not None else 0
stop = stop if stop is not None else len(coords)
coords = coords[start:stop]
return coords

--- Unit tests for TableIterator.get_result ---

BASIC TEST CASES

def test_basic_fixed_returns_func_result():
# Fixed type, should call func with start, stop, where
store = DummyStore()
s = DummyFixed()
def func(start, stop, where):
return (start, stop, where)
ti = TableIterator(store, s, func, where=[1,2,3], nrows=10)
codeflash_output = ti.get_result(); result = codeflash_output # 1.61μs -> 1.21μs (33.3% faster)

def test_basic_table_coordinates_false():
# Table type, coordinates=False, should call func with start, stop, where
store = DummyStore()
s = DummyTable()
def func(start, stop, where):
return (start, stop, where)
ti = TableIterator(store, s, func, where=[4,5,6], nrows=10)
codeflash_output = ti.get_result(); result = codeflash_output # 1.55μs -> 1.15μs (35.4% faster)

def test_chunksize_with_non_table_raises_typeerror():
# chunksize set, but s is not a Table, should raise TypeError
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return []
ti = TableIterator(store, s, func, where=None, nrows=10, chunksize=2)
with pytest.raises(TypeError):
ti.get_result() # 1.68μs -> 1.65μs (1.94% faster)

def test_coordinates_true_with_non_table_raises_typeerror():
# coordinates=True, but s is not a Table, should raise TypeError
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return []
ti = TableIterator(store, s, func, where=None, nrows=10)
with pytest.raises(TypeError):
ti.get_result(coordinates=True) # 1.57μs -> 1.76μs (11.1% slower)

def test_auto_close_closes_store():
# auto_close True, should close store after get_result
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return "done"
ti = TableIterator(store, s, func, where=None, nrows=5, auto_close=True)
codeflash_output = ti.get_result(); result = codeflash_output # 1.99μs -> 1.70μs (17.2% faster)

def test_start_stop_none_defaults():
# Table type, nrows=7, start/stop None, should default to start=0, stop=7
store = DummyStore()
s = DummyTable(coords=list(range(7)))
def func(start, stop, where):
return (start, stop, where)
ti = TableIterator(store, s, func, where=None, nrows=7, start=None, stop=None)
codeflash_output = ti.get_result(); result = codeflash_output # 1.59μs -> 1.16μs (37.0% faster)

def test_stop_larger_than_nrows_is_capped():
# Table type, stop > nrows, should cap stop at nrows
store = DummyStore()
s = DummyTable(coords=list(range(5)))
def func(start, stop, where):
return (start, stop, where)
ti = TableIterator(store, s, func, where=None, nrows=5, start=0, stop=10)
codeflash_output = ti.get_result(); result = codeflash_output # 1.49μs -> 1.15μs (29.4% faster)

def test_large_fixed_result():
# Fixed type, large where list, should call func and return correct result
store = DummyStore()
s = DummyFixed()
where = list(range(1000))
def func(start, stop, where):
return sum(where)
ti = TableIterator(store, s, func, where=where, nrows=1000)
codeflash_output = ti.get_result(); result = codeflash_output # 4.40μs -> 4.02μs (9.41% faster)

#------------------------------------------------
import pytest # used for our unit tests
from pandas.io.pytables import TableIterator

Minimal stubs for dependencies used by TableIterator.get_result

class DummyStore:
def init(self):
self.closed = False
def close(self):
self.closed = True

class DummyFixed:
is_table = False

class DummyTable(DummyFixed):
is_table = True
def init(self, coords=None):
self._coords = coords if coords is not None else list(range(10))
def read_coordinates(self, where=None, start=None, stop=None):
# Simulate coordinate selection
if start is None:
start = 0
if stop is None:
stop = len(self._coords)
# If where is a list of coordinates, filter accordingly
if isinstance(where, list):
return [c for c in self._coords if c in where and start <= c < stop]
# Otherwise, just slice
return self._coords[start:stop]

----------- UNIT TESTS BELOW ------------

Basic test cases

def test_basic_fixed_returns_func_result():
"""Basic: get_result returns func result for Fixed storer."""
store = DummyStore()
s = DummyFixed()
def func(start, stop, where):
return [start, stop, where]
ti = TableIterator(store, s, func, where="abc", nrows=10, start=1, stop=5)
codeflash_output = ti.get_result(); result = codeflash_output # 1.74μs -> 1.33μs (30.9% faster)

def test_basic_table_coordinates_false():
"""Basic: get_result returns func result for Table storer with coordinates=False."""
store = DummyStore()
s = DummyTable()
def func(start, stop, where):
return [start, stop, where]
ti = TableIterator(store, s, func, where=[2,3,4], nrows=10, start=2, stop=5)
codeflash_output = ti.get_result(); result = codeflash_output # 1.45μs -> 1.16μs (25.2% faster)

def test_edge_fixed_with_coordinates_true_raises():
"""Edge: coordinates=True on Fixed storer should raise TypeError."""
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return []
ti = TableIterator(store, s, func, where=None, nrows=10)
with pytest.raises(TypeError):
ti.get_result(coordinates=True) # 2.09μs -> 2.07μs (1.01% faster)

def test_edge_table_with_chunksize_on_fixed_raises():
"""Edge: chunksize on Fixed storer should raise TypeError."""
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return []
ti = TableIterator(store, s, func, where=None, nrows=10, chunksize=2)
with pytest.raises(TypeError):
ti.get_result() # 1.45μs -> 1.52μs (4.66% slower)

def test_edge_auto_close():
"""Edge: auto_close=True should close store after get_result."""
store = DummyStore()
s = DummyFixed()
def func(start, stop, where): return "done"
ti = TableIterator(store, s, func, where=None, nrows=10, auto_close=True)
codeflash_output = ti.get_result(); result = codeflash_output # 2.15μs -> 1.68μs (28.4% faster)

Large scale test cases

def test_large_table_coordinates_false():
"""Large: Table storer with coordinates=False, func receives full where."""
store = DummyStore()
coords = list(range(1000))
s = DummyTable(coords=coords)
def func(start, stop, where):
# Should receive the full where list
return where
ti = TableIterator(store, s, func, where=list(range(500)), nrows=1000, start=0, stop=1000)
codeflash_output = ti.get_result(coordinates=False); result = codeflash_output # 1.91μs -> 1.49μs (27.9% 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-mhvyf7s3 and push.