Comparing DataFrame with columns with mixed types to a scalar should fail

[michcio1234]

Code Sample, a copy-pastable example if possible

>>> s = pd.Series(['foo', 1])

>>> s > 1  # fails as expected
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/mdank/anaconda3/envs/pd-mixed/lib/python3.6/site-packages/pandas/core/ops.py", line 879, in wrapper
    res = na_op(values, other)
  File "/home/mdank/anaconda3/envs/pd-mixed/lib/python3.6/site-packages/pandas/core/ops.py", line 783, in na_op
    result = _comp_method_OBJECT_ARRAY(op, x, y)
  File "/home/mdank/anaconda3/envs/pd-mixed/lib/python3.6/site-packages/pandas/core/ops.py", line 763, in _comp_method_OBJECT_ARRAY
    result = lib.scalar_compare(x, y, op)
  File "pandas/_libs/lib.pyx", line 706, in pandas._libs.lib.scalar_compare
TypeError: '>' not supported between instances of 'str' and 'int'

>>> s.to_frame() > 1  # should also fail, gives wrong result instead
      0
0  True
1  True

Problem description

String to int comparison doesn't fail when comparing a DataFrame. It fails when comparing Series with mixed types, but in case of DataFrame it just silently produces wrong result.

Expected Output

TypeError in both cases - Series and DataFrame.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-104-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.14.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[jbrockmendel]

Closed by #22163.

[michcio1234]

Thanks for fixing it!