BUG: Respect Filtered Categorical in Crosstab
Closes #12298 by stripping the `Categorical` elements of their original categories so that only their included values are considered during the counting / aggregation.

this should put `NaN` where the categories are not represented, then `dropna` can control this.
 Why would we want `dropna` to control this?  What advantage would you have by counting unrepresented categories as `NaN`?

Also, not sure how best to efficiently implement that since you would have to iterate to check which categories are not represented and then replace them all with `NaN`?  Gymnastics much?
 ```
In [19]: result = pd.crosstab(df_filtered.col0, df_filtered.col1)

In [20]: result.iloc[2] = np.nan

In [21]: result
Out[21]: 
col1    1    2    3
col0               
a     2.0  0.0  0.0
b     1.0  1.0  0.0
c     NaN  NaN  NaN
```
 of course the default is `dropna` to maybe this should simply drop all of these (but if `dropna=False`) then should leave.
 1) Don't see an explanation as to why keeping `NaN` is useful.
2) Your code won't work.  When you call `df.pivot_table`, there will be no `NaN`'s to fill at that point.  The data you will see is `{rowname: Categorical,...}`
 ## [Current coverage](https://codecov.io/gh/pydata/pandas/pull/13073?src=pr) is **84.14%**

> Merging [#13073](https://codecov.io/gh/pydata/pandas/pull/13073?src=pr) into [master](https://codecov.io/gh/pydata/pandas/branch/master?src=pr) will increase coverage by **+<.01%**

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 @gfyoung I know you keep updating this, but:
- I think you need to deal with `dropna=False` and 
- simply converting to `object` (and listifying) is not a satisfying soln

so need to map out _what_ the output should actually be. haven't really though this thru, but
I think _categorical_ actually means something, you DO want to propogate it. (or at least we do in some situations). So need to figure out if this is one of those.

like others to weigh in here.

@jorisvandenbossche @janschulz 
 @jreback : for some reason, there was a merge conflict on my branch locally that wasn't showing up a GitHub, hence the update.  In any case, I think your point of view hinges essentially on whether or not preserving `Categorical` information is needed.  As a contributor, I generally would side with the user (in this case, whomever filed the issue) unless there is a strong argument against, which IMHO I have not seen either here or in the issue.
 @jorisvandenbossche @janschulz : any thoughts on what @jreback has said?
 > I think categorical actually means something, you DO want to propogate it. (or at least we do in some situations).

I'm firmly in that camp: if I have categoricals and some of them are not used, I want counts for that category show up as "0". So from my perspective https://github.com/pydata/pandas/issues/12298 works as intended and I would reject this premise (https://github.com/pydata/pandas/issues/12298#issuecomment-183136443) and the row should not be dropped:

> if we accept the premise that we don't want to always represent categorical results (IOW we treat them like regular results), then a filtered row should be dropped.

The only exception I can think of is if there is any way to distinguish between a empty categorical because of the filter and simply a not used category in the original data. But I don't think this is possible.
 @janschulz : I wouldn't have major issues leaving the behaviour as is, for I was in the same camp as you are when I saw this issue as well.  We could then just close this PR as well as the issue if there is enough consensus that this is indeed "correct" behaviour.

@jreback ?  @jorisvandenbossche ?
 I follow @janschulz in this one
 Alright, seems like consensus is to close this PR as well as #12298, which is perfectly fine with me.  @jreback (or someone else with write access) can do the honours?
 @gfyoung I tell you what. How about a nice example and explanation in the docs where cross tab is?

We'll close this one and the issue.
 @jreback : Sounds good.  Will do.
