duplicated() (and drop_duplicates()) incorrectly identifying unique/different rows as duplicates
#### Code Sample, a copy-pastable example if possible

```python

import pandas as pd

s = pd.Series.from_csv('duplicated_int64-bug.csv')

d = {}
non_dupe_python = set()
for k, val in s.iteritems():
    if k not in d:
        d[k] = val
    else:
        if val != d[k]:
            non_dupe_python.add(k)

dup = s.duplicated(keep=False)
has_2_entries = dup.groupby(level=0).count()
has_2_entries = has_2_entries[has_2_entries>1].index

non_dupe_pd = set(dup[dup==0].index & has_2_entries)
print "Non-dupes (Python, %d): %r" % (len(non_dupe_python), non_dupe_python)
print "Non-dupes (Series.duplicated, %d): %r" % (len(non_dupe_pd), non_dupe_pd)
print "False duplicates: %r" % (non_dupe_python-non_dupe_pd)

print "\nTwo false duplicates (problem here)"

print s.loc[1793]
print dup.loc[1793]

print s.loc[1795]
print dup.loc[1795]

print "\nA correctly-identified non-duplicate"
print s.loc[6080]
print dup.loc[6080]

print "\nA correctly-identified duplicate"
print s.loc[1]
print dup.loc[1]

```

[duplicated_int64-bug.csv.zip](https://github.com/pandas-dev/pandas/files/3270864/duplicated_int64-bug.csv.zip)

My output:
```
Non-dupes (Python, 9): set([6080, 1793, 1795, 6084, 1797, 1798, 5968, 5975, 6086])
Non-dupes (Series.duplicated, 7): set([6080, 6084, 1797, 1798, 5968, 5975, 6086])
False duplicates: set([1793, 1795])

Two false duplicates (problem here)
1793    1334122803464982
1793    1334122794298518
dtype: int64
1793    True
1793    True
dtype: bool
1795    1334122803464982
1795    1334122794298518
dtype: int64
1795    True
1795    True
dtype: bool

A correctly-identified non-duplicate
6080    91475737356800
6080    91475737482944
dtype: int64
6080    False
6080    False
dtype: bool

A correctly-identified duplicate
1    93251501583580
1    93251501583580
dtype: int64
1    True
1    True
dtype: bool
```
#### Problem description

Using 0.24.2 I was comparing two ~6,000-row DataFrames before and after some modifications and looking for modified rows by using concat and then drop_duplicates (keep=False, although IIRC the issue also happens with other arguments to keep) and found that it was reporting false duplicates (i.e. missing rows that were in fact modified.)

Stepping into ```drop_duplicates()```, then ```duplicated()```, then at the output of ```get_group_index()``` (a series of key->int64 pairs which looks like a total ordering of the DataFrame that I basically dumped into the attached csv) shows a problem most likely in ```duplicated_int64``` which looks like a Cython or native module (which unfortunately I don't have the cycles or toolchain to look into but maybe there's a false hash collision?)

The repro and csv above includes ~12000 int64->int64 pairs with a variety of duplicated and non-duplicated pairs and some keys that only appear once.

I couldn't find a similar report although this appears to be similar in principle to https://github.com/pandas-dev/pandas/issues/11864 (problem in ```duplicated_int64```) and wasn't fixed by that. Repro above should be able to reproduce & isolate the problem.

#### Expected Output

As shown above the two actual values for index 1793 are different and should not be reported as duplicate rows (i.e. ```dup.loc[1793]``` should return ```False```)

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.24.2
pytest: None
pip: 18.0
setuptools: 38.4.0
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.3.1
openpyxl: 2.5.2
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml.etree: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>


