Inconsistent Series/scalar comparison behavior based upon the scalar's type

[naftaliharris]

It's pretty unexpected that the behavior of x <= pd.Series(...) depends on whether x is a python float or a np.float64:

>>> import pandas as pd
>>> import numpy as np
>>> 5 <= pd.Series(range(10))
0    False
1    False
2    False
3    False
4    False
5     True
6     True
7     True
8     True
9     True
dtype: bool
>>> np.float64(5) <= pd.Series(range(10))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 588, in wrapper
    if len(self) != len(other):
TypeError: len() of unsized object
>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-54-virtual
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.16.2
nose: 1.3.3
Cython: None
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 0.12.1
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: 3.3.2
bs4: None
html5lib: None
httplib2: 0.7.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.2 (dt dec pq3 ext)
>>>

[naftaliharris]

Note that the issue isn't present on the other side:

>>> pd.Series(range(10)) >= 5
0    False
1    False
2    False
3    False
4    False
5     True
6     True
7     True
8     True
9     True
dtype: bool
>>> pd.Series(range(10)) >= np.float64(5)
0    False
1    False
2    False
3    False
4    False
5     True
6     True
7     True
8     True
9     True
dtype: bool

Apologies if this is a duplicate!

[shoyer]

I can replicate this on 0.16.2 This definitely looks like a bug to me -- I guess we just didn't have test coverage for this.

[jreback]

dupe of #9369 thanks for the report though!

[naftaliharris]

Excellent, thanks!