performance when "combining" categories
I have a performance question that is a follow-up to [this SO question](https://stackoverflow.com/questions/32262982/pandas-combining-multiple-categories-into-one). I apologize for the repeat, but my question is more focused on the performance issue, not how to do it. 

So suppose I have a `categorical` with many categories. I'm trying to reduce the number of categories efficiently. 

For example,

```python
import pandas as pd
import numpy as np

x = pd.Categorical(np.random.choice(['apple', 'banana', 'cheese', 'dates', 'eggs'], 10000000))
```

I want group all the fruits together, so I'd like to do something like this... 

```
x.map({'apple': 'fruit', 'banana': 'fruit', 'cheese': 'cheese', 'dates': 'fruit', 'eggs': 'eggs'})
```
The above line actually throws an error because `Categorical.map` requires a function, not a dict.

I *can* do something like this... 

```python
%time x = pd.Series(x).map({'apple': 'fruit', 'banana': 'fruit', 'cheese': 'cheese', 'dates': 'fruit', 'eggs': 'eggs'})
%time pd.Categorical(x)
```

However, there feels like there is large overhead in converting the categorical to a series, doing the map, then converting back to a categorical. 

I tried some other tricks like using `Categorical.remove_categories` for the old categories, then add the new category, then `fillna`, and its a little better, but feels a little hacky. 

Is there a more efficient way? 

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.22.0
pytest: 3.5.0
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 0.19.1
pyarrow: 0.8.0
xarray: 0.10.0
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.5.0+dev
dateutil: 2.6.1
pytz: 2017.2
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
</details>
