0.25.1 pandas.core.frame.DataFrame.eval() erroring-out on Python 3.7 (but not on Python 3.6)

[JohnMount]

Code Sample

import sys
import pandas
import numpy

print("# sys.version: " + sys.version)
print("# pandas.__version__: " + pandas.__version__)
print("# numpy.__version__: " + numpy.__version__)

d = pandas.DataFrame({
    "a": [True, False],
    "b": [1, 2],
    "c": [3, 4]})


pandas_eval_env = {
            "if_else": lambda c, x, y: numpy.where(c, x, y),
        }

d.eval("@if_else(a, 1, c)", local_dict=pandas_eval_env, global_dict=None)

Problem description

For Python 3.6 the above code runs. For Python 3.7 it errors-out with "TypeError: unhashable type: 'numpy.ndarray'". If I am returning the wrong type (an array instead of a named series or dataframe) I apologize.

Expected Output

For Python 3.6 we get what I expected:

array([1, 4])

For Python 3.7 0.25.1 pandas we get the following:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 3300, in eval
    return _eval(expr, inplace=inplace, **kwargs)
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/eval.py", line 327, in eval
    ret = eng_inst.evaluate()
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 70, in evaluate
    res = self._evaluate()
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 118, in _evaluate
    _check_ne_builtin_clash(self.expr)
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/engines.py", line 27, in _check_ne_builtin_clash
    names = expr.names
  File "/Users/johnmount/opt/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 850, in names
    return frozenset([self.terms.name])
TypeError: unhashable type: 'numpy.ndarray'

Note: both failing and successful runs are using Pandas 0.25.1 and numpy 1.17.2.

Edit: Pandas 0.25.3 with Python 3.7.5, numpy 1.17.3 works fine. My question then is: is there a work around for when we are using Pandas 0.25.1?

Output of pd.show_versions()

Working version:

sys.version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 13:42:17) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
pandas.__version__:0.25.1
numpy.__version__: 1.17.2
Out[4]: array([1, 4])
pandas.show_versions()
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 17.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8
pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.3.1
setuptools       : 41.6.0.post20191030
Cython           : None
pytest           : 5.2.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

Failing version:

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 17.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.2.1
hypothesis       : None
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.9
tables           : 3.5.2
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.1

[mroeschke]

This looks to work on main and looks like a dependency issue. Since we require newer version of these libraries, closing