ENH: add dropna argument to pivot_table
fixes #3820

```
a = np.array(['foo', 'foo', 'foo', 'bar', 'bar', 'foo', 'foo'], dtype=object)
b = np.array(['one', 'one', 'two', 'one', 'two', 'two', 'two'], dtype=object)
c = np.array(['dull', 'dull', 'dull', 'dull', 'dull', 'shiny', 'shiny'], dtype=object)

In [11]: pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'], drop_na=False)
Out[11]:
b     one          two
c    dull  shiny  dull  shiny
a
bar     1      0     1      0
foo     2      0     1      2
```

Also same argument for `pivot_table`.

Question: Why did you  choose `drop_na` instead of `dropna` which is the  name of the data frame method? 
 @jtratner whoops!
 I've also realised that doing something like `np.testing.assert_equal(m[:3], m)` doesn't raise (I guess I need to use `np.testing.assert_equal(m[:3].values, m.values)`...
 @hayd `assert_frame_equal`? or `assert_almost_equal`?
 ah didn't realize it was a multiindex. (internal representation is an empty ndarray)
 imo len(some_multiindex) should behave as if some_multiindex is a 2d array.
 any reason why that shouldn't be the case?
 `__len__` needs to match `__iter__` (generally) - I need to look at it
 I think it needs to be:

``` python
def __len__(self):
    return len(self.levels) * len(self.labels)
```
 That way `len(list(iter(multi_index))) == len(multi_index)`
 that doesn't match `__iter__` since `__iter__` is over each tuple. should be

``` python
def __len__(self):
    return len(self.values)
```
 e.g., if there are 10 level and 10 labels then `len` will return 100 which makes sense as maybe a `size` attr like an ndarray, but really `MultiIndex` straddles the array of tuple interp and the 2d array interp
 Oh, I thought len(self.labels) \* len(self.levels) was equal to
len(self.values)?

On Tue, Jul 2, 2013 at 9:09 PM, Phillip Cloud notifications@github.comwrote:

> e.g., if there are 10 level and 10 labels then len will return 100 which
> makes sense as maybe a size attr like an ndarray, but really MultiIndexstraddles the array of tuple interp and the 2d array interp
> 
> —
> Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/pull/4106#issuecomment-20389878
> .
 Nope, I'm wrong :P

On Tue, Jul 2, 2013 at 9:22 PM, Jeffrey Tratner
jeffrey.tratner@gmail.comwrote:

> Oh, I thought len(self.labels) \* len(self.levels) was equal to
> len(self.values)?
> 
> On Tue, Jul 2, 2013 at 9:09 PM, Phillip Cloud notifications@github.comwrote:
> 
> > e.g., if there are 10 level and 10 labels then len will return 100 which
> > makes sense as maybe a size attr like an ndarray, but really MultiIndexstraddles the array of tuple interp and the 2d array interp
> > 
> > —
> > Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/pull/4106#issuecomment-20389878
> > .
 @cpcloud no reason really... I was away from the internet when wrote the test case and forgot what it was.
 Converted test case to example from previous issue.

cartesian_product is slightly slower (when passing to MultIndex) for small inputs but considerably faster for large ones:

```
In [23]: %timeit pd.MultiIndex.from_tuples(list(product(list('ABC'), [1, 2])))1000 loops, best of 3: 399 us per loop

In [24]: %timeit pd.MultiIndex.from_arrays(cartesian_product([list('ABC'), [1, 2]]))
1000 loops, best of 3: 541 us per loop
```

```
X = list('ABC' * 100)
Y = [1,2] * 100

In [27]: %timeit pd.MultiIndex.from_arrays(cartesian_product([X, Y]))
100 loops, best of 3: 8.1 ms per loop

In [28]: %timeit pd.MultiIndex.from_tuples(list(product(X, Y)))
10 loops, best of 3: 21.5 ms per loop
```
 merge?
