concat with categorical variables
#### Code Sample, a copy-pastable example if possible
import pandas as pd
cat_type1 = pd.api.types.CategoricalDtype(categories=['a','b'],ordered=False)
cat_type2 = pd.api.types.CategoricalDtype(categories=['b','a'],ordered=False)
cat_type3 = pd.api.types.CategoricalDtype(categories=['b','a','c'],ordered=False)
#problem number 1:
print(cat_type1==cat_type2) #these are different (the codes representing each category are different), but report as identical
print(cat_type1==cat_type3) #these are clearly different

s1 = pd.Series(['a','b','b','a'],dtype=cat_type1)
print(s1)
s2 = pd.Series(['a','b','b','a'],dtype=cat_type2)
print(s2)
s3 = pd.Series(['a','b','b','a'],dtype=cat_type3)
s1_s2 = pd.concat([s1,s2])
print(s1_s2)
s1_s3 = pd.concat([s1,s3])
print(s1_s3)

#### Problem description
We're concatenating Series with different categorical dtypes. We have 3 classes of problem:

1. unordered categoricaldtypes are not quite identical if the order of the categories in the index of the dtype are different - the underlying code values are different. But perhaps this is meant to be abstracted away. However, it may be the underlying issue / misunderstanding here.

2. When we concatenate 2 series with different categorical dtypes (as in s1_s3), it should error according to the docs. It doesn't, but it does something sane: it converts to object and accurately reflects the data (even if it loses the categorical nature of the data).

3. When we concatenate 2 series with "identical" categorical dtypes but where the order of the codes to those categories is different (as in s1_s2) it doesn't error and it doesn't convert to object. It doesn't spot the problem, preserves the underlying code values and applies the first categorical, completely corrupting the data.

This has knock-on implications in dask also.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
</details>
