BUG: Fix boolean comparison with a DataFrame on the lhs, and a list/tuple on the rhs GH4576
closes #4576

```
In [1]: df = DataFrame(np.arange(6).reshape((3,2)))

In [2]:  b = np.array([2, 2])

In [3]:  b_r = np.atleast_2d([2,2])

In [4]:  b_c = b_r.T

In [5]: df>b
Out[5]: 
       0      1
0  False  False
1  False   True
2   True   True

In [6]: df.values>b
Out[6]: 
array([[False, False],
       [False,  True],
       [ True,  True]], dtype=bool)

In [7]: df>b_r
Out[7]: 
       0      1
0  False  False
1  False   True
2   True   True

In [8]: df.values>b_r
Out[8]: 
array([[False, False],
       [False,  True],
       [ True,  True]], dtype=bool)

In [9]: df>b_c
ValueError: cannot broadcast shape [(3, 2)] with block values [(2, 1)]

In [10]: df.values>b_c
ValueError: operands could not be broadcast together with shapes (3,2) (2,1) 

In [11]: df == b
Out[11]: 
       0      1
0  False  False
1   True  False
2  False  False

In [12]: df.values == b
Out[12]: 
array([[False, False],
       [ True, False],
       [False, False]], dtype=bool)

In [13]: df==b_r
Out[13]: 
       0      1
0  False  False
1   True  False
2  False  False

In [14]: df.values==b_r
Out[14]: 
array([[False, False],
       [ True, False],
       [False, False]], dtype=bool)
```

Numpy does weird things like this (bottom example), but we will raise
I believe it is actually a NotImplemented Type which is interpreted as False (but still very weird)

```
In [15]: df==b_c
ValueError: cannot broadcast shape [(3, 2)] with block values [(2, 1)]

In [16]: df.values==b_c
Out[16]: False
```

with list/tuple

```
In [2]: df>(2,2)
ValueError: operands could not be broadcast together with shapes (2,3) (2) 

In [3]: df>(2,2,2)
Out[3]: 
       0      1
0  False  False
1  False   True
2   True   True

In [4]: df>[2,2,2]
Out[4]: 
       0      1
0  False  False
1  False   True
2   True   True

In [5]: df==[2,2,2]
Out[5]: 
       0      1
0  False  False
1   True  False
2  False  False
```

This will work with a frame with non integer labels too

```
In [6]: df = DataFrame(np.arange(6).reshape((3,2)),columns=list('AB'),index=list('abc'))

In [7]: df==[2,2,2]
Out[7]: 
       A      B
a  False  False
b   True  False
c  False  False
```
