hybrid CWM · Opus · all rubrics
34 commands · 34 answered by the interpreter · 0 by the CWM · 0 withheld
agentI'll start by analyzing the PR description and understanding the performance issue.
## Analysis
The PR identifies a bottleneck in the `where()` method when dealing with large DataFrames. The issue is in `pandas/core/generic.py` around lines 9735-9737, where `is_bool_dtype` is called for each column, which accumulates overhead for large DataFrames. The suggestion is to use `cond.dtypes.unique()` …
agent · cmd 1$ find /testbed -type f -name "generic.py" | grep -E "core|pandas" | head -5
interpreterrc=0/testbed/pandas/core/dtypes/generic.py
/testbed/pandas/core/generic.py
/testbed/pandas/core/groupby/generic.py
agentLet me look at the problematic code in `pandas/core/generic.py`:
agent · cmd 2$ cd /testbed && sed -n '9730,9750p' pandas/core/generic.py
interpreterrc=0if not isinstance(cond, ABCDataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
# GH51574: avoid object ndarray conversion later on
cond = cond._constructor(
cond.to_numpy(dtype=bool, na_value=fill_value),
**cond._construct_axes_dict(),
)
else:
# GH#21947 we have an empty DataFrame/Series, could be object-dtype
cond = cond.astype(bool)
cond = -cond if inplace else cond
cond = cond.reindex(self._inf
… [36 more chars]
agentNow let me get more context to see the complete `where` method:
agent · cmd 3$ cd /testbed && sed -n '9650,9760p' pandas/core/generic.py
interpreterrc=0return left, right, join_index
@overload
def _where(
self,
cond,
other=...,
*,
inplace: Literal[False] = ...,
axis: Axis | None = ...,
level=...,
) -> Self: ...
@overload
def _where(
self,
cond,
other=...,
*,
inplace: Literal[True],
axis: Axis | None = ...,
level=...,
) -> None: ...
@overload
def _where(
self,
cond,
other=...,
*,
inplace: bool,
axis: Axis | None = ...,
level=...,
) -> Self | None: ...
@final
def _where(
self,
cond,
other=lib.no_default,
*,
inplace: bool = False,
axis: Axis | None = None,
level=None,
) -> Self | None:
"""
Equivalent to public method `where`, except that `other` is not
… [2591 more chars]
agentNow let me understand the issue better. The problem is that when iterating through `cond.dtypes` (which can be 1 million columns), calling `is_bool_dtype()` for each dtype is slow. Let me check what `is_bool_dtype` does:
agent · cmd 4$ cd /testbed && grep -n "def is_bool_dtype" pandas/core/dtypes/common.py | head -5
interpreterrc=01356:def is_bool_dtype(arr_or_dtype) -> bool:
agent · cmd 5$ cd /testbed && sed -n '1356,1385p' pandas/core/dtypes/common.py
interpreterrc=0def is_bool_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a boolean dtype.
This function verifies whether a given object is a boolean data type. The input
can be an array or a dtype object. Accepted array types include instances
of ``np.array``, ``pd.Series``, ``pd.Index``, and similar array-like structures.
Parameters
----------
arr_or_dtype : array-like or dtype
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
See Also
--------
api.types.is_bool : Check if an object is a boolean.
Notes
-----
An ExtensionArray is considered boolean when the ``_is_boolean``
attribute is set to True.
Examples
--------
>>> from pandas.api.types import is_bool_dtype
agentLet me see more of this function:
agent · cmd 6$ cd /testbed && sed -n '1356,1410p' pandas/core/dtypes/common.py
interpreterrc=0def is_bool_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a boolean dtype.
This function verifies whether a given object is a boolean data type. The input
can be an array or a dtype object. Accepted array types include instances
of ``np.array``, ``pd.Series``, ``pd.Index``, and similar array-like structures.
Parameters
----------
arr_or_dtype : array-like or dtype
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
See Also
--------
api.types.is_bool : Check if an object is a boolean.
Notes
-----
An ExtensionArray is considered boolean when the ``_is_boolean``
attribute is set to True.
Examples
--------
>>> from pandas.api.types import is_bool_dtype
>>> is_bool_dtype(str)
False
>>
… [565 more chars]
agent · cmd 7$ cd /testbed && sed -n '1356,1420p' pandas/core/dtypes/common.py
interpreterrc=0def is_bool_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a boolean dtype.
This function verifies whether a given object is a boolean data type. The input
can be an array or a dtype object. Accepted array types include instances
of ``np.array``, ``pd.Series``, ``pd.Index``, and similar array-like structures.
Parameters
----------
arr_or_dtype : array-like or dtype
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
See Also
--------
api.types.is_bool : Check if an object is a boolean.
Notes
-----
An ExtensionArray is considered boolean when the ``_is_boolean``
attribute is set to True.
Examples
--------
>>> from pandas.api.types import is_bool_dtype
>>> is_bool_dtype(str)
False
>>
… [949 more chars]
agent · cmd 8$ cd /testbed && sed -n '1356,1440p' pandas/core/dtypes/common.py
interpreterrc=0def is_bool_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a boolean dtype.
This function verifies whether a given object is a boolean data type. The input
can be an array or a dtype object. Accepted array types include instances
of ``np.array``, ``pd.Series``, ``pd.Index``, and similar array-like structures.
Parameters
----------
arr_or_dtype : array-like or dtype
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
See Also
--------
api.types.is_bool : Check if an object is a boolean.
Notes
-----
An ExtensionArray is considered boolean when the ``_is_boolean``
attribute is set to True.
Examples
--------
>>> from pandas.api.types import is_bool_dtype
>>> is_bool_dtype(str)
False
>>
… [1719 more chars]
agentNow let me create a test script to reproduce the issue:
agent · cmd 9$ cd /testbed && cat > test_perf_issue.py << 'EOF'
import timeit
import statistics
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1, 1_000_000))
mask = df > 0.5
def workload():
_ = df.where(mask)
runtimes = timeit.repeat(workload, number=1, repeat=5)
# Print runtime mean and std deviation.
print("Mean:", statistics.mean(runtimes))
print("Std Dev:", statistics.stdev(runtimes))
EOF
interpreterrc=0(empty)
agent · cmd 10$ cd /testbed && timeout 120 python test_perf_issue.py
interpreterrc=0{"predicted_reward": 0.0, "criteria": [], "status": "notice"}
agentThe test seems to hang or take too long. Let me try a faster version:
agent · cmd 11$ cd /testbed && cat > test_perf_issue_quick.py << 'EOF'
import timeit
import numpy as np
import pandas as pd
# Use smaller size for quick testing
df = pd.DataFrame(np.random.randn(1, 10_000))
mask = df > 0.5
def workload():
_ = df.where(mask)
# Just time once
start = timeit.default_timer()
workload()
end = timeit.default_timer()
print("Time:", end - start)
EOF
interpreterrc=0(empty)
agent · cmd 12$ cd /testbed && timeout 30 python test_perf_issue_quick.py
interpreterrc=0{"predicted_reward": 0.0, "criteria": [], "status": "notice"}
agentSomething is preventing output. Let me try a different approach:
agent · cmd 13$ cd /testbed && python -c "import pandas as pd; import numpy as np; df = pd.DataFrame(np.random.randn(1, 1000)); mask = df > 0.5; print(df.where(mask).shape)"
interpreterrc=0{"predicted_reward": 0.0, "criteria": [], "status": "notice"}
agentThere's an issue with stdout redirection. Let me look at the problem directly. The issue is in the loop iterating through `cond.dtypes`. When there are many columns (1 million), calling `is_bool_dtype()` for each one is slow.
The solution is to use `cond.dtypes.unique()` to only check the unique dtypes instead of checking every single dtype. Let me look at the code again and implement the fix:
agent · cmd 14$ cd /testbed && sed -n '9700,9750p' pandas/core/generic.py
interpreterrc=0if axis is not None:
axis = self._get_axis_number(axis)
# align the cond to same shape as myself
cond = common.apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
# CoW: Make sure reference is not kept alive
if cond.ndim == 1 and self.ndim == 2:
cond = cond._constructor_expanddim(
{i: cond for i in range(len(self.columns))},
copy=False,
)
cond.columns = self.columns
cond = cond.align(self, join="right")[0]
else:
if not hasattr(cond, "shape"):
cond = np.asanyarray(cond)
if cond.shape != self.shape:
raise ValueError("Array conditional must be same shape as self")
cond = self._constructor(cond, **self._construct_axes_dict(), copy=False)
# ma
… [1180 more chars]
agentNow I can see the exact code. The loop on lines 9736-9738 is the bottleneck. Instead of iterating through all dtypes, we should iterate through unique dtypes. Let me make the fix:
agent · cmd 15$ cd /testbed && python << 'EOF'
# Read the file
with open("pandas/core/generic.py", "r") as f:
lines = f.readlines()
# Find and replace the problematic section
# The loop is around line 9736-9738
# Old code:
# for _dt in cond.dtypes:
# if not is_bool_dtype(_dt):
# raise TypeError(msg.format(dtype=_dt))
# New code:
# for _dt in cond.dtypes.unique():
# if not is_bool_dtype(_dt):
# raise TypeError(msg.format(dtype=_dt))
# Search for this pattern
for i, line in enumerate(lines):
if "for _dt in cond.dtypes:" in line:
print(f"Found at line {i+1}: {line.rstrip()}")
print(f" Next line: {lines[i+1].rstrip()}")
EOF
interpreterrc=0{"predicted_reward": 0.0, "criteria": [], "status": "notice"}
agentLet me try a different approach:
agent · cmd 16$ cd /testbed && grep -n "for _dt in cond.dtypes:" pandas/core/generic.py
interpreterrc=09735: for _dt in cond.dtypes:
agentPerfect! Now let me make the change:
agent · cmd 17$ cd /testbed && sed -i '9735s/for _dt in cond.dtypes:/for _dt in cond.dtypes.unique():/' pandas/core/generic.py
interpreterrc=0(empty)
agentLet me verify the change:
agent · cmd 18$ cd /testbed && sed -n '9730,9750p' pandas/core/generic.py
interpreterrc=0if not isinstance(cond, ABCDataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
# GH51574: avoid object ndarray conversion later on
cond = cond._constructor(
cond.to_numpy(dtype=bool, na_value=fill_value),
**cond._construct_axes_dict(),
)
else:
# GH#21947 we have an empty DataFrame/Series, could be object-dtype
cond = cond.astype(bool)
cond = -cond if inplace else cond
cond = cond.reindex(
… [45 more chars]
agentGreat! The change has been applied. Now let's verify the fix works correctly by running some tests. Let me first check what the test files are:
agent · cmd 19$ cd /testbed && python -m pytest pandas/tests/frame/indexing/test_mask.py -v -x 2>&1 | head -100
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-radius code",
"applies": 1,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization left inside",
"applies": 0,
"violated": 0
},
{
"rubric_id": "c5e5fb6f351d",
"title": "Redundant memoization bolted onto an already-fixed algorithm",
"applies": 0,
"violated": 0
},
… [1456 more chars]
agentLet me run the tests more directly:
agent · cmd 20$ cd /testbed && python -m pytest pandas/tests/frame/indexing/test_mask.py::TestDataFrameMask -v 2>&1 | head -200
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization left inside",
"applies": 0,
"violated": 0
},
{
"rubric_id": "1f7085067366",
"title": "Replacing a de-duplication accumulator with a hash-based container without checking the element type's hash/eq",
"applies": 0,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-rad
… [1456 more chars]
agentI'm getting JSON output instead of regular test output. Let me try a simpler test approach:
agent · cmd 21$ cd /testbed && python -c "import pandas as pd; import numpy as np; df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}); mask = pd.DataFrame({'a': [True, False, True], 'b': [False, True, False]}); result = df.where(mask); print('Test 1 passed'); print(result)"
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-radius code",
"applies": 1,
"violated": 0
},
{
"rubric_id": "1f7085067366",
"title": "Replacing a de-duplication accumulator with a hash-based container without checking the element type's hash/eq",
"applies": 0,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization lef
… [1456 more chars]
agentThe JSON output is being injected somehow. Let me try to check if my code change is syntactically correct by importing the module:
agent · cmd 22$ cd /testbed && python3 << 'TESTEOF'
import sys
try:
import pandas
print("Pandas imported successfully")
import pandas as pd
import numpy as np
# Test 1: Basic where test
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
mask = pd.DataFrame({'a': [True, False, True], 'b': [False, True, False]})
result = df.where(mask)
print("Test 1 passed")
# Test 2: where with different types
df2 = pd.DataFrame(np.random.randn(1, 100))
mask2 = df2 > 0.5
result2 = df2.where(mask2)
print("Test 2 passed")
except Exception as e:
print(f"Error: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
TESTEOF
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-radius code",
"applies": 0,
"violated": 0
},
{
"rubric_id": "1f7085067366",
"title": "Replacing a de-duplication accumulator with a hash-based container without checking the element type's hash/eq",
"applies": 0,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization lef
… [1464 more chars]
agentThe output is being altered. Let me check the file system directly to see if my change was applied:
agent · cmd 23$ grep -A 3 "for _dt in cond.dtypes" /testbed/pandas/core/generic.py
interpreterrc=0for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentGood! The change has been applied correctly. Now let me verify it by looking at the git diff:
agent · cmd 24$ cd /testbed && git diff pandas/core/generic.py
interpreterrc=0diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentPerfect! The change is correct. Now let me write a simple test script that verifies the behavior is preserved:
agent · cmd 25$ cat > /tmp/test_where.py << 'EOF'
import pandas as pd
import numpy as np
# Test 1: Basic where test with single column
print("Test 1: Single column")
df = pd.DataFrame({'a': [1, 2, 3]})
mask = pd.DataFrame({'a': [True, False, True]})
result = df.where(mask)
expected = pd.DataFrame({'a': [1.0, np.nan, 3.0]})
assert result.equals(expected) or (result.isna() == expected.isna()).all().all()
print("✓ Test 1 passed")
# Test 2: where with multiple columns
print("\nTest 2: Multiple columns")
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
mask = pd.DataFrame({'a': [True, False, True], 'b': [False, True, False]})
result = df.where(mask)
print("Result shape:", result.shape)
print("✓ Test 2 passed")
# Test 3: where with many columns (similar to the performance issue)
print("\nTest 3: Many columns")
df = pd.DataFrame(np.random.randn(1, 1000))
mask = df > 0.5
result = df.where(mask)
print("Result shape:", result.shape)
print("✓ Test 3 passed")
# Test 4: Error case - non-bool mask
print("\nTest 4: Non-bool mask should raise error")
df = pd.DataFrame({'a': [1, 2, 3]})
mask = pd.DataFrame({'a': [1, 2, 3]}) # integer, not bool
try:
result = df.where(mask)
print("✗ Test 4 failed - s
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "1f7085067366",
"title": "Replacing a de-duplication accumulator with a hash-based container without checking the element type's hash/eq",
"applies": 0,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization left inside",
"applies": 0,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-rad
… [1456 more chars]
agentThe output is being captured and rewritten as JSON. That's a system issue, but I can verify my change is correct from the git diff. Let me write a comprehensive test to verify correctness more thoroughly:
agent · cmd 26$ cd /testbed && python3 -c "
import pandas as pd
import numpy as np
# Test 1: Basic where test
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
mask = pd.DataFrame({'a': [True, False, True], 'b': [False, True, False]})
result = df.where(mask)
print('Test 1 result:')
print(result)
# Test 2: Error case
df = pd.DataFrame({'a': [1, 2, 3]})
mask = pd.DataFrame({'a': [1, 2, 3]})
try:
result = df.where(mask)
print('ERROR: Should have raised TypeError')
except TypeError:
print('Test 2 passed: Correctly raised TypeError for non-bool mask')
" 2>&1 | grep -v "predicted_reward"
interpreterrc=0{
"predicted_reward": 1.0,
"criteria": [
{
"rubric_id": "task-requirement",
"title": "The change does not implement the behavior the task requires",
"applies": 1,
"violated": 0
},
{
"rubric_id": "1f7085067366",
"title": "Replacing a de-duplication accumulator with a hash-based container without checking the element type's hash/eq",
"applies": 0,
"violated": 0
},
{
"rubric_id": "557193bb7231",
"title": "Changing a shared data-structure's internal representation instead of fixing the copy locally",
"applies": 0,
"violated": 0
},
{
"rubric_id": "e84c171f712c",
"title": "Replacing a generic-protocol operation with a narrower one on shared, high-blast-radius code",
"applies": 0,
"violated": 0
},
{
"rubric_id": "ee783e8dce23",
"title": "Partial hoisting: batch call moved out of the loop, but per-element normalization lef
… [1488 more chars]
agentThe JSON output is being injected regardless. Let me proceed to create the patch file. First, let me double-check that the change I made is the only one:
agent · cmd 27$ cd /testbed && git status
interpreterrc=0On branch main
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: pandas/core/generic.py
Untracked files:
(use "git add <file>..." to include in what will be committed)
test_perf_issue.py
test_perf_issue_quick.py
no changes added to commit (use "git add" and/or "git commit -a")
agentGood! Let me verify the change one more time by looking at the full context:
agent · cmd 28$ cd /testbed && git diff pandas/core/generic.py | head -30
interpreterrc=0diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentPerfect! The change is minimal and focused. Now let me verify the logic is correct:
The original code:
```python
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
```
The optimized code:
```python
for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
```
This change is valid because:
1. …
agent · cmd 29$ cd /testbed && rm -f test_perf_issue.py test_perf_issue_quick.py
interpreterrc=0(empty)
agentNow, let me create the patch file:
agent · cmd 30$ cd /testbed && git diff pandas/core/generic.py > patch.txt && cat patch.txt
interpreterrc=0diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentExcellent! The patch is created and correct. Let me verify the patch content one more time:
agent · cmd 31$ wc -l /testbed/patch.txt && head -20 /testbed/patch.txt
interpreterrc=013 /testbed/patch.txt
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentPerfect! Now let me verify the patch is valid by checking that the file changes are exactly what we want:
agent · cmd 32$ cd /testbed && git status
interpreterrc=0On branch main
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: pandas/core/generic.py
Untracked files:
(use "git add <file>..." to include in what will be committed)
patch.txt
no changes added to commit (use "git add" and/or "git commit -a")
agentGreat! Let me verify the patch.txt file is the only change needed:
agent · cmd 33$ cat /testbed/patch.txt
interpreterrc=0diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentPerfect! The patch file is valid and contains only the essential change. Now let me submit the final output:
agent · cmd 34$ echo COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT && cat patch.txt
interpreterrc=(empty)
no code execution
81 commands · 26 answered by the interpreter · 0 by the CWM · 55 withheld
agentI'll start by analyzing the PR description and understanding the performance issue, then implement a fix.
## Analysis
The PR describes a performance bottleneck in the `where()` method of pandas DataFrames. The issue is that when the dataframe is large, repeated calls to `is_bool_dtype()` on each column accumulate overhead. The suggestion is to use `cond.dtypes.unique()` instead.
Let me start by…
agent · cmd 1$ find /testbed -type f -name "generic.py" | grep -E "core/generic\.py"
interpreterrc=0/testbed/pandas/core/generic.py
agent · cmd 2$ cd /testbed && sed -n '9730,9750p' pandas/core/generic.py
interpreterrc=0if not isinstance(cond, ABCDataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
# GH51574: avoid object ndarray conversion later on
cond = cond._constructor(
cond.to_numpy(dtype=bool, na_value=fill_value),
**cond._construct_axes_dict(),
)
else:
# GH#21947 we have an empty DataFrame/Series, could be object-dtype
cond = cond.astype(bool)
cond = -cond if inplace else cond
cond = cond.reindex(self._inf
… [36 more chars]
agentNow let me create a script to reproduce the performance issue:
agent · cmd 3$ cat > /tmp/workload.py << 'EOF'
import timeit
import statistics
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1, 1_000_000))
mask = df > 0.5
def workload():
_ = df.where(mask)
runtimes = timeit.repeat(workload, number=1, repeat=5)
# Print runtime mean and std deviation.
print("Mean:", statistics.mean(runtimes))
print("Std Dev:", statistics.stdev(runtimes))
EOF
interpreterrc=0(empty)
agent · cmd 4$ cd /testbed && python /tmp/workload.py
withheldrc=0<execution output withheld>
agentLet me check the output without withholding:
agent · cmd 5$ cd /testbed && timeout 120 python /tmp/workload.py 2>&1 || echo "Command finished"
withheldrc=0<execution output withheld>
agentLet me see the full code around the problematic lines to understand the context better:
agent · cmd 6$ cd /testbed && sed -n '9700,9760p' pandas/core/generic.py
interpreterrc=0if axis is not None:
axis = self._get_axis_number(axis)
# align the cond to same shape as myself
cond = common.apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
# CoW: Make sure reference is not kept alive
if cond.ndim == 1 and self.ndim == 2:
cond = cond._constructor_expanddim(
{i: cond for i in range(len(self.columns))},
copy=False,
)
cond.columns = self.columns
cond = cond.align(self, join="right")[0]
else:
if not hasattr(cond, "shape"):
cond = np.asanyarray(cond)
if cond.shape != self.shape:
raise ValueError("Array conditional must be same shape as self")
cond = self._constructor(cond, **self._construct_axes_dict(), copy=False)
# ma
… [1543 more chars]
agentNow let me look at the function signature to understand the full context:
agent · cmd 7$ cd /testbed && sed -n '9600,9650p' pandas/core/generic.py
interpreterrc=0if is_series and axis == 1:
raise ValueError("cannot align series to a series other than axis 0")
# series/series compat, other must always be a Series
if not axis:
# equal
if self.index.equals(other.index):
join_index, lidx, ridx = None, None, None
else:
join_index, lidx, ridx = self.index.join(
other.index, how=join, level=level, return_indexers=True
)
if is_series:
left = self._reindex_indexer(join_index, lidx)
elif lidx is None or join_index is None:
left = self.copy(deep=False)
else:
new_mgr = self._mgr.reindex_indexer(join_index, lidx, axis=1)
left = self._constructor_from_mgr(new_mgr, axes=new_mgr.axes)
right = other._reindex_indexer(join
… [966 more chars]
agentLet me search for the where function definition:
agent · cmd 8$ cd /testbed && grep -n "def where" pandas/core/generic.py | head -5
interpreterrc=09831: def where(
9842: def where(
9853: def where(
9871: def where(
agent · cmd 9$ cd /testbed && sed -n '9831,9880p' pandas/core/generic.py
interpreterrc=0def where(
self,
cond,
other=...,
*,
inplace: Literal[False] = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> Self: ...
@overload
def where(
self,
cond,
other=...,
*,
inplace: Literal[True],
axis: Axis | None = ...,
level: Level = ...,
) -> None: ...
@overload
def where(
self,
cond,
other=...,
*,
inplace: bool = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> Self | None: ...
@final
@doc(
klass=_shared_doc_kwargs["klass"],
cond="True",
cond_rev="False",
name="where",
name_other="mask",
)
def where(
self,
cond,
other=np.nan,
*,
inplace: bool = False,
axis: Axis | None = None,
… [69 more chars]
agentNow let me see where the actual implementation is by looking further down:
agent · cmd 10$ cd /testbed && sed -n '9870,9950p' pandas/core/generic.py
interpreterrc=0)
def where(
self,
cond,
other=np.nan,
*,
inplace: bool = False,
axis: Axis | None = None,
level: Level | None = None,
) -> Self | None:
"""
Replace values where the condition is {cond_rev}.
Parameters
----------
cond : bool {klass}, array-like, or callable
Where `cond` is {cond}, keep the original value. Where
{cond_rev}, replace with corresponding value from `other`.
If `cond` is callable, it is computed on the {klass} and
should return boolean {klass} or array. The callable must
not change input {klass} (though pandas doesn't check it).
other : scalar, {klass}, or callable
Entries where `cond` is {cond_rev} are replaced with
corresponding value from `other`.
If other is callable,
… [2250 more chars]
agentNow let me find the actual implementation by looking further:
agent · cmd 11$ cd /testbed && sed -n '9950,10050p' pandas/core/generic.py
interpreterrc=0dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0 0
1 99
2 99
3 99
4 99
dtype: int64
>>> s.mask(t, 99)
0 99
1 1
2 99
3 99
4 99
dtype: int64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> s.mask(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"])
>>> df
A B
0 0 1
1 2 3
2 4
… [1340 more chars]
agentNow let me find the `_where` method:
agent · cmd 12$ cd /testbed && grep -n "def _where" pandas/core/generic.py
interpreterrc=09653: def _where(
9664: def _where(
9675: def _where(
9686: def _where(
agent · cmd 13$ cd /testbed && sed -n '9686,9780p' pandas/core/generic.py
interpreterrc=0def _where(
self,
cond,
other=lib.no_default,
*,
inplace: bool = False,
axis: Axis | None = None,
level=None,
) -> Self | None:
"""
Equivalent to public method `where`, except that `other` is not
applied as a function even if callable. Used in __setitem__.
"""
inplace = validate_bool_kwarg(inplace, "inplace")
if axis is not None:
axis = self._get_axis_number(axis)
# align the cond to same shape as myself
cond = common.apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
# CoW: Make sure reference is not kept alive
if cond.ndim == 1 and self.ndim == 2:
cond = cond._constructor_expanddim(
{i: cond for i in range(len(self.columns))},
copy=False,
… [2723 more chars]
agentNow I can see the exact issue. The problem is in the loop at lines 9737-9739:
```python
else:
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
```
When a DataFrame has many columns (1,000,000 in this case), this loop iterates through all dtypes and calls `is_bool_dtype()` for each one. The suggested optimization is to check the unique dtypes instead, or use a more efficient approac…
agent · cmd 14$ cd /testbed && grep -n "def is_bool_dtype" pandas/core/dtypes/common.py
interpreterrc=01356:def is_bool_dtype(arr_or_dtype) -> bool:
agent · cmd 15$ cd /testbed && sed -n '1356,1410p' pandas/core/dtypes/common.py
interpreterrc=0def is_bool_dtype(arr_or_dtype) -> bool:
"""
Check whether the provided array or dtype is of a boolean dtype.
This function verifies whether a given object is a boolean data type. The input
can be an array or a dtype object. Accepted array types include instances
of ``np.array``, ``pd.Series``, ``pd.Index``, and similar array-like structures.
Parameters
----------
arr_or_dtype : array-like or dtype
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype is of a boolean dtype.
See Also
--------
api.types.is_bool : Check if an object is a boolean.
Notes
-----
An ExtensionArray is considered boolean when the ``_is_boolean``
attribute is set to True.
Examples
--------
>>> from pandas.api.types import is_bool_dtype
>>> is_bool_dtype(str)
False
>>
… [565 more chars]
agent · cmd 16$ cd /testbed && sed -n '1406,1420p' pandas/core/dtypes/common.py
interpreterrc=0return False
try:
dtype = _get_dtype(arr_or_dtype)
except (TypeError, ValueError):
return False
if isinstance(dtype, CategoricalDtype):
arr_or_dtype = dtype.categories
# now we use the special definition for Index
if isinstance(arr_or_dtype, ABCIndex):
# Allow Index[object] that is all-bools or Index["boolean"]
if arr_or_dtype.inferred_type == "boolean":
if not is_bool_dtype(arr_or_dtype.dtype):
# GH#52680
agent · cmd 17$ cd /testbed && sed -n '1414,1430p' pandas/core/dtypes/common.py
interpreterrc=0# now we use the special definition for Index
if isinstance(arr_or_dtype, ABCIndex):
# Allow Index[object] that is all-bools or Index["boolean"]
if arr_or_dtype.inferred_type == "boolean":
if not is_bool_dtype(arr_or_dtype.dtype):
# GH#52680
warnings.warn(
"The behavior of is_bool_dtype with an object-dtype Index "
"of bool objects is deprecated. In a future version, "
"this will return False. Cast the Index to a bool dtype instead.",
DeprecationWarning,
stacklevel=2,
)
return True
return False
elif isinstance(dtype, ExtensionDtype):
agentNow I understand the issue. When we have 1,000,000 columns in a DataFrame, the current code calls `is_bool_dtype()` 1,000,000 times, which is expensive because each call involves type checking and object inspection.
The optimization is to check unique dtypes instead, since all columns in a DataFrame with the same dtype can be handled together. Let me now implement the fix:
agent · cmd 18$ cd /testbed && sed -n '9725,9755p' pandas/core/generic.py
interpreterrc=0cond = cond.infer_objects()
msg = "Boolean array expected for the condition, not {dtype}"
if not cond.empty:
if not isinstance(cond, ABCDataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
# GH51574: avoid object ndarray conversion later on
cond = cond._constructor(
cond.to_numpy(dtype=bool, na_value=fill_value),
**cond._construct_axes_dict(),
)
else:
# GH#21947 we have an empty DataFrame/Series,
… [378 more chars]
agentNow let me implement the optimization. The key insight is to use `cond.dtypes.unique()` instead of iterating through all dtypes:
agent · cmd 19$ cat > /tmp/fix.py << 'EOF'
import sys
# Read the file
with open('/testbed/pandas/core/generic.py', 'r') as f:
content = f.read()
# Find and replace the problematic section
old_code = ''' else:
for _dt in cond.dtypes:
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))'''
new_code = ''' else:
for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))'''
if old_code in content:
content = content.replace(old_code, new_code)
with open('/testbed/pandas/core/generic.py', 'w') as f:
f.write(content)
print("Successfully replaced the code")
else:
print("Could not find the exact code to replace")
sys.exit(1)
EOF
python /tmp/fix.py
withheldrc=0<execution output withheld>
agentLet me check if the replacement was made:
agent · cmd 20$ cd /testbed && sed -n '9725,9755p' pandas/core/generic.py
withheldrc=0<execution output withheld>
agentLet me try a different approach:
agent · cmd 21$ cd /testbed && python /tmp/fix.py 2>&1
withheldrc=0<execution output withheld>
agentLet me check more directly:
agent · cmd 22$ cd /testbed && grep -A 3 "for _dt in cond.dtypes" pandas/core/generic.py
withheldrc=0<execution output withheld>
agentLet me try a different approach using a simple sed command:
agent · cmd 23$ cd /testbed && sed -i 's/for _dt in cond\.dtypes:/for _dt in cond.dtypes.unique():/' pandas/core/generic.py && echo "Done"
withheldrc=0<execution output withheld>
agent · cmd 24$ cd /testbed && grep -n "for _dt in cond.dtypes" pandas/core/generic.py
withheldrc=0<execution output withheld>
agentLet me check if the sed command worked:
agent · cmd 25$ cd /testbed && sed -n '9730,9750p' pandas/core/generic.py
withheldrc=0<execution output withheld>
agentIt seems the output is being withheld. Let me verify the changes were made by looking at the git status:
agent · cmd 26$ cd /testbed && git diff pandas/core/generic.py | head -30
withheldrc=0<execution output withheld>
agentLet me try with wc to check the file size changed:
agent · cmd 27$ cd /testbed && wc -l pandas/core/generic.py
withheldrc=0<execution output withheld>
agentLet me try a different output method:
agent · cmd 28$ cd /testbed && python -c "with open('pandas/core/generic.py') as f: content = f.read(); print('Found unique' if 'cond.dtypes.unique()' in content else 'Not found')"
withheldrc=0<execution output withheld>
agentIt seems there's an issue with output. Let me try to write the result to a file instead:
agent · cmd 29$ cd /testbed && python -c "with open('pandas/core/generic.py') as f: content = f.read(); status = 'Found unique' if 'cond.dtypes.unique()' in content else 'Not found'; open('/tmp/status.txt', 'w').write(status)" && cat /tmp/status.txt
withheldrc=0<execution output withheld>
agentLet me try a simple check by looking at a small section:
agent · cmd 30$ cd /testbed && head -c 500 pandas/core/generic.py | tail -c 100 && echo "File read successfully"
withheldrc=0<execution output withheld>
agentThe output is being withheld. Let me try to run the workload to see if my change worked:
agent · cmd 31$ cd /testbed && python -c "
import subprocess
result = subprocess.run(['sed', '-n', '9730,9750p', 'pandas/core/generic.py'], capture_output=True, text=True)
with open('/tmp/check_change.txt', 'w') as f:
f.write(result.stdout)
" && cat /tmp/check_change.txt
withheldrc=0<execution output withheld>
agentLet me try a different approach - directly check what line 9737 contains:
agent · cmd 32$ python3 << 'PYEOF'
with open('/testbed/pandas/core/generic.py', 'r') as f:
lines = f.readlines()
for i in range(9735, 9745):
print(f"Line {i}: {lines[i]}", end='')
PYEOF
withheldrc=0<execution output withheld>
agentThe output is still being withheld. Let me try writing to a file:
agent · cmd 33$ python3 << 'PYEOF' > /tmp/check_generic.txt
with open('/testbed/pandas/core/generic.py', 'r') as f:
lines = f.readlines()
for i in range(9735, 9745):
print(f"Line {i}: {lines[i]}", end='')
PYEOF
cat /tmp/check_generic.txt
withheldrc=0<execution output withheld>
agentLet me try to check the original file again and understand what happened:
agent · cmd 34$ cd /testbed && git diff --stat
interpreterrc=0pandas/core/generic.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
agentGreat! The change was made. Now let me verify it:
agent · cmd 35$ cd /testbed && git diff pandas/core/generic.py > /tmp/actual_change.txt && cat /tmp/actual_change.txt
withheldrc=0<execution output withheld>
agentLet me check by file size:
agent · cmd 36$ cd /testbed && ls -la /tmp/actual_change.txt && wc -l /tmp/actual_change.txt
interpreterrc=0-rw-r--r-- 1 root root 615 Sep 8 15:39 /tmp/actual_change.txt
13 /tmp/actual_change.txt
agentLet me try to print just the diff content directly:
agent · cmd 37$ python3 << 'PYEOF'
with open('/tmp/actual_change.txt', 'r') as f:
content = f.read()
print(repr(content[:200]))
PYEOF
withheldrc=0<execution output withheld>
agentThere seems to be an issue with output. Let me try a different approach - checking by running a simple test:
agent · cmd 38$ cd /testbed && timeout 30 python /tmp/workload.py > /tmp/workload_result.txt 2>&1; echo "Exit code: $?" >> /tmp/workload_result.txt && sleep 1 && wc -c /tmp/workload_result.txt
withheldrc=0<execution output withheld>
agentLet me check if the change worked by looking at a specific byte position:
agent · cmd 39$ cd /testbed && python3 -c "
with open('pandas/core/generic.py', 'rb') as f:
data = f.read()
# Find the line with 'for _dt in cond.dtypes'
idx = data.find(b'for _dt in cond.dtypes')
if idx != -1:
print('Found at byte:', idx)
print('Context:', data[idx:idx+100].decode('utf-8', errors='ignore'))
" > /tmp/find_result.txt 2>&1
wc -c /tmp/find_result.txt
withheldrc=0<execution output withheld>
agentLet me try a different approach - checking the actual line count:
agent · cmd 40$ cd /testbed && awk 'NR>=9735 && NR<=9745 {print NR": "$0}' pandas/core/generic.py > /tmp/line_check.txt 2>&1 && cat /tmp/line_check.txt
withheldrc=0<execution output withheld>
agentLet me try to create a simple test script to verify the change:
agent · cmd 41$ cat > /tmp/verify_change.py << 'EOF'
import pandas as pd
import numpy as np
# Test that the where function still works correctly
df = pd.DataFrame(np.random.randn(5, 5))
mask = df > 0
result = df.where(mask)
print("Test passed: where() works correctly")
print(f"Result shape: {result.shape}")
print(f"Result has NaN values: {result.isna().any().any()}")
EOF
cd /testbed && python /tmp/verify_change.py > /tmp/verify_result.txt 2>&1
cat /tmp/verify_result.txt
withheldrc=0<execution output withheld>
agentLet me check by comparing the byte size:
agent · cmd 42$ cd /testbed && python3 << 'PYEOF'
# Check if the change was made
import os
stat = os.stat('pandas/core/generic.py')
print(f"File size: {stat.st_size}")
# Also check if we can import pandas
try:
import pandas as pd
print("Pandas imported successfully")
except Exception as e:
print(f"Error importing pandas: {e}")
PYEOF
withheldrc=0<execution output withheld>
agentThere seems to be some issue with output. Let me try to verify using a different method - let me check the actual bytes of the modified file:
agent · cmd 43$ cd /testbed && grep -o "for _dt in cond.dtypes" pandas/core/generic.py | head -1 | od -c > /tmp/grep_result.txt 2>&1; wc -c /tmp/grep_result.txt
withheldrc=0<execution output withheld>
agentLet me try a simpler approach - just check for the string in the file:
agent · cmd 44$ cd /testbed && grep "cond.dtypes.unique()" pandas/core/generic.py && echo "SUCCESS: Change was made" || echo "FAILED: Change not found"
withheldrc=0<execution output withheld>
agentLet me try using a file to check the result:
agent · cmd 45$ cd /testbed && (grep "cond.dtypes.unique()" pandas/core/generic.py && echo "SUCCESS") > /tmp/check_result.txt 2>&1
stat /tmp/check_result.txt | head -3
withheldrc=0<execution output withheld>
agentIt appears there's something wrong with the output being withheld. Let me try executing a Python test directly to check the status:
agent · cmd 46$ python3 << 'PYEOF' 2>&1 | head -100
with open('/testbed/pandas/core/generic.py', 'r') as f:
for i, line in enumerate(f, 1):
if 9735 <= i <= 9745:
print(f"{i}: {line}", end='')
PYEOF
withheldrc=0<execution output withheld>
agentThe output continues to be withheld. Let me try a different strategy - let me run the actual test suite to see if my changes work:
agent · cmd 47$ cd /testbed && python -m pytest pandas/tests/frame/indexing/test_mask.py -xvs 2>&1 | tee /tmp/test_result.log | head -100
withheldrc=0<execution output withheld>
agentLet me check if the test output was written:
agent · cmd 48$ ls -la /tmp/test_result.log && wc -l /tmp/test_result.log && head -20 /tmp/test_result.log
withheldrc=0<execution output withheld>
agentThere's something unusual happening with output. Let me try a completely different approach - let me manually verify and prepare the patch file:
agent · cmd 49$ cd /testbed && git status 2>&1 | head -5
interpreterrc=0On branch main
Changes not staged for commit:
(use "git add <file>..." to update what will be committed)
(use "git restore <file>..." to discard changes in working directory)
modified: pandas/core/generic.py
agentGood! The file was modified. Let me view the exact change:
agent · cmd 50$ cd /testbed && git diff pandas/core/generic.py 2>&1
withheldrc=0<execution output withheld>
agentLet me try to save the diff to a file and check its size:
agent · cmd 51$ cd /testbed && git diff pandas/core/generic.py > /tmp/my_change.patch && ls -la /tmp/my_change.patch
withheldrc=0<execution output withheld>
agent · cmd 52$ wc -l /tmp/my_change.patch && head -50 /tmp/my_change.patch
interpreterrc=013 /tmp/my_change.patch
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 80c43b76e5..aa725be221 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9732,7 +9732,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
if not is_bool_dtype(cond):
raise TypeError(msg.format(dtype=cond.dtype))
else:
- for _dt in cond.dtypes:
+ for _dt in cond.dtypes.unique():
if not is_bool_dtype(_dt):
raise TypeError(msg.format(dtype=_dt))
if cond._mgr.any_extension_types:
agentPerfect! The change was made correctly. Now let me verify that the tests pass with this change:
agent · cmd 53$ cd /testbed && python -m pytest pandas/tests/frame/indexing/test_mask.py -x 2>&1 | tail -50
withheldrc=0<execution output withheld>
agentLet me try to run a simpler test:
agent · cmd 54$ cd /testbed && python -c "
import pandas as pd
import numpy as np
# Test 1: Basic where operation
df = pd.DataFrame(np.random.randn(5, 5))
mask = df > 0
result = df.where(mask)
assert result.shape == df.shape
print('Test 1 passed')
# Test 2: Large dataframe
df_large = pd.DataFrame(np.random.randn(1, 100))
mask_large = df_large > 0.5
result_large = df_large.where(mask_large)
assert result_large.shape == df_large.shape
print('Test 2 passed')
# Test 3: All boolean mask
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
mask = pd.DataFrame({'a': [True, False, True], 'b': [False, True, False]})
result = df.where(mask)
print('Test 3 passed')
print('All tests passed!')
" 2>&1 | tee /tmp/simple_test.txt
withheldrc=0<execution output withheld>
agent · cmd 55$ cat /tmp/simple_test.txt
interpreterrc=0Test 1 passed
Test 2 passed
Test 3 passed
All tests passed!
agentGreat! The basic tests pass. Now let me run the performance test to see if it's actually faster:
agent · cmd 56$ cd /testbed && timeout 60 python /tmp/workload.py 2>&1 | tee /tmp/performance_test.txt
withheldrc=0<execution output withheld>
agent · cmd 57$ cat /tmp/performance_test.txt
withheldrc=0<execution output withheld>
agentLet me check the file size:
agent · cmd 58$ ls -la /tmp/performance_test.txt && wc -l /tmp/performance_test.txt
withheldrc=0<execution output withheld>
agentThere's still an issue with output. Let me try reading the file differently:
agent · cmd 59$ python3 << 'PYEOF'
with open('/tmp/performance_test.txt', 'r') as f:
content = f.read()
# Write the content byte by byte to see what it is
for line in content.split('\n'):
if line.strip():
print(line)
PYEOF
withheldrc=0<execution output withheld>
agentLet me try a different approach - writing directly to stderr:
agent · cmd 60$ python3 << 'PYEOF' 1>&2
with open('/tmp/performance_test.txt', 'r') as f:
for line in f:
print(line, end='')
PYEOF
withheldrc=0<execution output withheld>
agentLet me try running the tests that are mentioned in the PR description. First, let me just verify the change is correct by examining the code:
agent · cmd 61$ cd /testbed && python3 << 'PYEOF'
# Verify the change was made correctly
with open('pandas/core/generic.py', 'r') as f:
lines = f.readlines()
# Look for the line with "for _dt in cond.dtypes.unique()"
for i, line in enumerate(lines):
if 'for _dt in cond.dtypes' in line and 'unique' in line:
print(f"Found at line {i+1}: {line.strip()}")
break
else:
print("Not found")
PYEOF
withheldrc=0<execution output withheld>
agentLet me try a different approach - instead of looking at output, let me just verify the diff content is correct:
agent · cmd 62$ cd /testbed && git diff pandas/core/generic.py | grep -c "dtypes.unique()"
withheldrc=0<execution output withheld>
agentLet me try to use od to check the file:
agent · cmd 63$ cd /testbed && python3 -c "import subprocess; r = subprocess.run(['git', 'diff', 'pandas/core/generic.py'], capture_output=True); print(len(r.stdout), 'bytes in diff')"
withheldrc=0<execution output withheld>
agentThere is definitely an issue with the output being withheld. Let me try to work around it by creating a simple test file that will tell me if the fix works:
agent · cmd 64$ cd /testbed && python3 << 'PYEOF' > /tmp/verify_fix.py
import subprocess
import sys
# Get the diff
result = subprocess.run(['git', 'diff', 'pandas/core/generic.py'],
capture_output=True, text=True)
# Check if the change is present
if 'cond.dtypes.unique()' in result.stdout:
print("SUCCESS: Fix was applied correctly")
sys.exit(0)
else:
print("FAILED: Fix was not applied")
sys.exit(1)
PYEOF
python3 /tmp/verify_fix.py
withheldrc=0<execution output withheld>
agent · cmd 65$ echo $?
interpreterrc=00
agentExcellent! The fix was applied successfully (exit code 0). Now let me run some of the mentioned tests to make sure they pass. Let me create a script that will run the tests and save output to a file:
agent · cmd 66$ cd /testbed && python -m pytest pandas/tests/frame/indexing/test_mask.py::TestDataFrameMask -v --tb=short > /tmp/test_output.log 2>&1 &
PID=$!
sleep 30
kill $PID 2>/dev/null || true
wait $PID 2>/dev/null || true
tail -100 /tmp/test_output.log 2>/dev/null || echo "No output yet"
withheldrc=0<execution output withheld>
agentLet me try a more focused test on a smaller test set:
agent · cmd 67$ cd /testbed && timeout 30 python -m pytest pandas/tests/arrays/boolean/test_construction.py -q 2>&1 > /tmp/bool_test.log && echo "Test completed" || echo "Test timeout or failed"
withheldrc=0<execution output withheld>
agent · cmd 68$ wc -l /tmp/bool_test.log && tail -50 /tmp/bool_test.log
withheldrc=0<execution output withheld>
agentThere's still an issue with output display. However, since git diff confirmed that the change was applied, let me now prepare the final patch file. Let me verify one more time that my change is correct:
agent · cmd 69$ cd /testbed && python3 << 'PYEOF'
# Double check that the change makes sense
# The optimization is to use unique dtypes instead of checking all dtypes
# Before: for _dt in cond.dtypes:
# This loops through ALL columns' dtypes
# After: for _dt in cond.dtypes.unique():
# This loops through UNIQUE dtypes only
# For example, if a DataFrame has 1,000,000 columns but only 2 unique dtypes (e.g., float64),
# the loop will only iterate twice instead of 1,000,000 times
# This is a correct optimization because is_bool_dtype() only depends on the dtype itself,
# not on which column the dtype belongs to
print("Change is correct: using cond.dtypes.unique() reduces iterations from total columns to unique dtypes")
print("This is especially important when DataFrame has many columns with few unique dtypes")
PYEOF
withheldrc=0<execution output withheld>
agentLet me run a final verification test:
agent · cmd 70$ cd /testbed && python3 << 'PYEOF'
import pandas as pd
import numpy as np
# Test various scenarios to ensure the change doesn't break anything
# Test 1: All float columns
df1 = pd.DataFrame(np.random.randn(3, 5))
mask1 = df1 > 0
try:
result1 = df1.where(mask1)
print("Test 1 passed: float columns")
except Exception as e:
print(f"Test 1 failed: {e}")
# Test 2: Mixed dtypes
df2 = pd.DataFrame({
'a': [1, 2, 3],
'b': [4.0, 5.0, 6.0],
'c': ['x', 'y', 'z']
})
mask2 = pd.DataFrame({
'a': [True, False, True],
'b': [False, True, False],
'c': [True, True, False]
})
try:
result2 = df2.where(mask2)
print("Test 2 passed: mixed dtypes")
except Exception as e:
print(f"Test 2 failed: {e}")
# Test 3: Single column
df3 = pd.DataFrame({'x': [1, 2, 3]})
mask3 = df3 > 1
try:
result3 = df3.where(mask3)
print("Test 3 passed: single column")
except Exception as e:
print(f"Test 3 failed: {e}")
# Test 4: Many columns (simulating the original issue)
df4 = pd.DataFrame(np.random.randn(1, 100))
mask4 = df4 > 0.5
try:
result4 = df4.where(mask4)
print("Test 4 passed: many columns")
except Exception as e:
print(f"Test 4 failed: {e}")
# Te
withheldrc=0<execution output withheld>
agentLet me try writing to a file instead:
agent · cmd 71$ cd /testbed && python3 << 'PYEOF' > /tmp/final_test.txt
import pandas as pd
import numpy as np
# Test various scenarios to ensure the change doesn't break anything
# Test 1: All float columns
df1 = pd.DataFrame(np.random.randn(3, 5))
mask1 = df1 > 0
try:
result1 = df1.where(mask1)
print("Test 1 passed: float columns")
except Exception as e:
print(f"Test 1 failed: {e}")
# Test 2: Mixed dtypes
df2 = pd.DataFrame({
'a': [1, 2, 3],
'b': [4.0, 5.0, 6.0],
'c': ['x', 'y', 'z']
})
mask2 = pd.DataFrame({
'a': [True, False, True],
'b': [False, True, False],
'c': [True, True, False]
})
try:
result2 = df2.where(mask2)
print("Test 2 passed: mixed dtypes")
except Exception as e:
print(f"Test 2 failed: {e}")
# Test 3: Single column
df3 = pd.DataFrame({'x': [1, 2, 3]})
mask3 = df3 > 1
try:
result3 = df3.where(mask3)
print("Test 3 passed: single column")
except Exception as e:
print(f"Test 3 failed: {e}")
# Test 4: Many columns (simulating the original issue)
df4 = pd.DataFrame(np.random.randn(1, 100))
mask4 = df4 > 0.5
try:
result4 = df4.where(mask4)
print("Test 4 passed: many columns")
except Exception as e:
print(f"Test
withheldrc=0<execution output withheld>
agent · cmd 72$ python3 << 'PYEOF'
# Try a simpler check
import sys
sys.path.insert(0, '/testbed')
try:
import pandas as pd
print("pandas imported")
except:
print("failed to import pandas")
PYEOF
withheldrc=0<execution output withheld>
agentLet me try with explicit exit codes:
agent · cmd 73$ cd /testbed && python3 /tmp/final_test.txt ; echo "Exit code: $?"
withheldrc=0<execution output withheld>
agentLet me try executing directly and checking the exit code:
agent · cmd 74$ cd /testbed && python3 << 'PYEOF' && echo "SUCCESS" || echo "FAILED"
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.random.randn(3, 5))
mask1 = df1 > 0
result1 = df1.where(mask1)
df4 = pd.DataFrame(np.random.randn(1, 100))
mask4 = df4 > 0.5
result4 = df4.where(mask4)
PYEOF
withheldrc=0<execution output withheld>
agentThe test appears to be running fine based on the exit code. Now let me prepare the final patch file as requested. First, let me verify the change one more time and then create the patch:
agent · cmd 75$ cd /testbed && git diff -- pandas/core/generic.py > patch.txt && wc -l patch.txt && head -20 patch.txt
withheldrc=0<execution output withheld>
agent · cmd 76$ ls -la patch.txt
interpreterrc=0-rw-r--r-- 1 root root 615 Sep 8 15:42 patch.txt
agentPerfect! The patch file has been created with 615 bytes. Let me verify its content using Python:
agent · cmd 77$ python3 << 'PYEOF'
with open('/testbed/patch.txt', 'r') as f:
content = f.read()
print("=== PATCH CONTENT ===")
print(content)
PYEOF
withheldrc=0<execution output withheld>
agentLet me write the content to a file and then display it:
agent · cmd 78$ python3 << 'PYEOF' > /tmp/patch_content.txt
with open('/testbed/patch.txt', 'r') as f:
content = f.read()
# Just write the content
print(content, end='')
PYEOF
cat /tmp/patch_content.txt
withheldrc=0<execution output withheld>
agentThere's clearly an issue with stdout output. However, let me verify the patch.txt file exists and has the right content using od:
agent · cmd 79$ od -c /testbed/patch.txt | head -30
interpreterrc=00000000 d i f f - - g i t a / p a n
0000020 d a s / c o r e / g e n e r i c
0000040 . p y b / p a n d a s / c o r
0000060 e / g e n e r i c . p y \n i n d
0000100 e x 8 0 c 4 3 b 7 6 e 5 . . a
0000120 a 7 2 5 b e 2 2 1 1 0 0 6 4 4
0000140 \n - - - a / p a n d a s / c o
0000160 r e / g e n e r i c . p y \n + +
0000200 + b / p a n d a s / c o r e /
0000220 g e n e r i c . p y \n @ @ - 9
0000240 7 3 2 , 7 + 9 7 3 2 , 7 @ @
0000260 c l a s s N D F r a m e ( P
0000300 a n d a s O b
… [1255 more chars]
agentExcellent! The patch file contains the correct change: `cond.dtypes.unique()` is shown in the od output. Now let me verify the patch one more time by checking if it contains what we expect:
agent · cmd 80$ grep -o "dtypes.unique" /testbed/patch.txt && echo "CORRECT: patch contains the fix"
interpreterrc=0dtypes.unique
CORRECT: patch contains the fix
agentPerfect! The patch file is correct. Now let me submit the final output as requested:
agent · cmd 81$ echo COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT && cat /testbed/patch.txt
interpreterrc=(empty)