.isin() raises TypeError for Series with > 1 million elements if argument has mixed dtypes
#### Code Sample, a copy-pastable example if possible

```python
In [2]: i = pd.Series(list('abcdefghijk'*10**5))

In [3]: alt = [-1, 'AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GR']*6

In [4]: res = i[:10**6].isin(alt)

In [5]: res = i[:10**6+1].isin(alt)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-f3e21d855671> in <module>()
----> 1 res = i[:10**6+1].isin(alt)

/home/pietro/nobackup/repo/pandas/pandas/core/series.py in isin(self, values)
   2458 
   2459         """
-> 2460         result = algorithms.isin(_values_from_object(self), values)
   2461         return self._constructor(result, index=self.index).__finalize__(self)
   2462 

/home/pietro/nobackup/repo/pandas/pandas/core/algorithms.py in isin(comps, values)
    421             comps = comps.astype(object)
    422 
--> 423     return f(comps, values)
    424 
    425 

/home/pietro/nobackup/repo/pandas/pandas/core/algorithms.py in <lambda>(x, y)
    401     f = lambda x, y: htable.ismember_object(x, values)
    402     if (_np_version_under1p8 and compat.PY3) or len(comps) > 1000000:
--> 403         f = lambda x, y: np.in1d(x, y)
    404     elif is_integer_dtype(comps):
    405         try:

/usr/lib/python3/dist-packages/numpy/lib/arraysetops.py in in1d(ar1, ar2, assume_unique, invert)
    399     if not assume_unique:
    400         ar1, rev_idx = np.unique(ar1, return_inverse=True)
--> 401         ar2 = np.unique(ar2)
    402 
    403     ar = np.concatenate((ar1, ar2))

/usr/lib/python3/dist-packages/numpy/lib/arraysetops.py in unique(ar, return_index, return_inverse, return_counts)
    212         aux = ar[perm]
    213     else:
--> 214         ar.sort()
    215         aux = ar
    216     flag = np.concatenate(([True], aux[1:] != aux[:-1]))

TypeError: unorderable types: str() > int()

```
#### Problem description

Although the length of ``alt`` also matters in some way, even with a completely different dataset the problem still started at 1M elements in the ``Series`` (or even ``Index``) being searched. By the way, triggering the error takes much more time than the successful operation.

Might be related to #13432 , although that one is unrelated to the length of the ``Series``.

#### Expected Output

Like ``In [4]``, just with one more element.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8

pandas: 0.19.0+783.gcd35d22a0
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1


</details>
