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BUG: Allow using numpy in DataFrame.eval and DataFrame.query via @-notation #58057

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Merged
merged 7 commits into from
Apr 8, 2024
Merged

BUG: Allow using numpy in DataFrame.eval and DataFrame.query via @-notation #58057

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b6722c5
Allow @np to be used within df.eval and df.query
domsmrz Mar 27, 2024
4051d36
Add whatsnew
domsmrz Mar 28, 2024
fd47a93
Merge branch 'main' into np-eval-fix
domsmrz Mar 28, 2024
e0b1e72
Fix typo
domsmrz Mar 29, 2024
81d9c7f
Test: skip checking names due to inconsistencies between OSes
domsmrz Mar 29, 2024
1642cdb
Elaborate futher on whatsnew message
domsmrz Apr 2, 2024
dee12b3
Fix the test by explicitly specifing engine.
domsmrz Apr 2, 2024
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
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Expand Up @@ -325,6 +325,7 @@ Bug fixes
- Fixed bug in :class:`SparseDtype` for equal comparison with na fill value. (:issue:`54770`)
- Fixed bug in :meth:`.DataFrameGroupBy.median` where nat values gave an incorrect result. (:issue:`57926`)
- Fixed bug in :meth:`DataFrame.cumsum` which was raising ``IndexError`` if dtype is ``timedelta64[ns]`` (:issue:`57956`)
- Fixed bug in :meth:`DataFrame.eval` and :meth:`DataFrame.query` which caused an exception when using numpy via ``@`` notation. (:issue:`58041`)
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I'm not sure if "when using numpy" is clear - what do you think of doing something like

when using NumPy attributes via @ notation, e.g. df.eval("@np.floor(a)")

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Updated as per suggestion, thanks

- Fixed bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)
- Fixed bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
- Fixed bug in :meth:`DataFrame.transform` that was returning the wrong order unless the index was monotonically increasing. (:issue:`57069`)
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2 changes: 1 addition & 1 deletion pandas/core/computation/ops.py
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Expand Up @@ -115,7 +115,7 @@ def _resolve_name(self):
res = self.env.resolve(local_name, is_local=is_local)
self.update(res)

if hasattr(res, "ndim") and res.ndim > 2:
if hasattr(res, "ndim") and isinstance(res.ndim, int) and res.ndim > 2:
raise NotImplementedError(
"N-dimensional objects, where N > 2, are not supported with eval"
)
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7 changes: 7 additions & 0 deletions pandas/tests/computation/test_eval.py
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Expand Up @@ -737,6 +737,13 @@ def test_and_logic_string_match(self):
assert pd.eval(f"{event.str.match('hello').a}")
assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}")

def test_using_numpy(self):
# GH 58041
df = Series([0.2, 1.5, 2.8], name="a").to_frame()
res = df.eval("@np.floor(a)")
expected = np.floor(df["a"])
tm.assert_series_equal(expected, res, check_names=False)
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Can you set the proper name on expected and then remove check_names=False.

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@domsmrz domsmrz Apr 2, 2024 •
edited
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Unfortunately, I can't. The issue is that df.eval(@np.floor(a)) will have different names in Linux unittests compared to Win/MacOS unittest. Meaning if I just remove check_names and:

  • leave as-is, the Linux unittests will fail
  • change the name of expected to "a", the Win & MacOS unittests will fail

Please see my previous comment and/or #58069 for a little bit more details.

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On a further thought, we can sort of work around the issue by explicitly stating the engine, which is arguably something we should do anyway. I've updated the PR accordingly -- and also moved the test into more fitting test file. Please take a look and let me know what you think.



# -------------------------------------
# gh-12388: Typecasting rules consistency with python
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