API: comparisons of categorical data and (scalar or list-like)

[jankatins]

From #8946:

If cat > scalar is allowed and cat == list also because it basically is doing a comparison of each line as if it was the scalar case, then by that logic, cat > list should also be allowed: each row in that comparison would treat the element from the list as a scalar.

On the other hand a scalar comparison with the categorical makes only sense if the scalar can be treated as a category (for any other value, it's basically a "not of the same type" comparison, which would raise on python3), so the scalar must be in categories and this should not work:

In[4]: df = pd.DataFrame({"a":[1,3,3,3,np.nan]})
In[6]: df["b"] = df.a.astype("category")
In[7]: df.b
Out[7]: 
0     1
1     3
2     3
3     3
4   NaN
Name: b, dtype: category
Categories (2, float64): [1 < 3]
In[8]: df.b > 2
Out[8]: 
0    False
1     True
2     True
3     True
4    False
Name: b, dtype: bool

Oh, one more thing: according to that thought, df.b == 2 (-> The "equality" case) should also NOT work, because 2 is not in categories and therefore a "different type".

Current code results in this:

In [5]: df.b==2
Out[5]: 
0    False
1    False
2    False
3    False
4    False
Name: b, dtype: bool

this is actually consistent (e.g. it returns False). On a comparison it shouldn't raise so this is a reasonable result. I think this is de-facto like the following and is useful.

In [7]: Series(['a','b','c'])==2
Out[7]: 
0    False
1    False
2    False
dtype: bool

[jankatins]

@jreback: is this Series(['a','b','c'])==2 work on Py2 and Py3? If it is working on both I'm ok with that, but I think I remember that this raises on Py3 and I think we should model the API similar (= it should raise on Py3).

[jreback]

Seems to work in py3 as well.

In [1]: Series(['a','b','c'])==2
Out[1]: 
0    False
1    False
2    False
dtype: bool

In [2]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.15.1-84-gf1b270f

[jbrockmendel]

AFAICT this works as expected. @jankatins can you confirm if this is still an issue?