Issue 1:
I wish I could say more about this, but it is with proprietary data. Basically, I have a DataFrame of 10 columns and around 7000 rows. When I call "duplicated" on 4 of the rows (3 have strings, 1 has a float), I get around 10 items being flagged as duplicate (5 pairs) when in fact, they are different.

This is sample text of what is being flagged:

Type Line LineString \
1885 else 832.0 (&temp32)->byte1 = (&temp32)->byte4
1895 do while 832.0 do while(0)
4515 enum 122.0 enum {QQ_ON_UNASSERTED = 0, QQ_ON_ASSERTED = 1}
4521 enum 167.0 enum {FIELD_RESET = 1, FIELD_TRIPPED = 0}

Parameter Filename
1885 temp32 arinc.c
1895 arinc.c
4515 QQ_ON_ASSERTED eeprom.c
4521 FIELD_TRIPPED eeprom.c

I know that duplicates are checked through hashing, but is there some way to, I don't know, compare a checksum or a more robust measurement to ensure that only duplicates are flagged? What is the chance that two items could be hashed to the same value?

By adding more of the fields to check the data I am able to prevent false duplicate flags.

I am using Pandas 0.18 from WinPython-64bit-3.4.4.2Qt5.

Issue 2:
Code Sample, a copy-pastable example if possible

# Your code here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates

t = np.arange(10)
ts = pd.datetime(2017, 11, 16) + pd.to_timedelta(t, 's')
df = pd.DataFrame(t, index=ts)

fig, ax = plt.subplots()
ax.plot(df)
# workaround:
# ax.plot(df.index.to_pydatetime(), df)

# format time axis
hfmt = dates.DateFormatter('%m/%d %H:%M')
ax.xaxis.set_major_formatter(hfmt)
fig.autofmt_xdate()
plt.show()

Problem description

Version: pandas-0.21.0

trace:

Traceback (most recent call last):

File "D:\Anaconda3\lib\site-packages\IPython\core\formatters.py", line 332, in __call__
return printer(obj)

File "D:\Anaconda3\lib\site-packages\IPython\core\pylabtools.py", line 237, in <lambda>
png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "D:\Anaconda3\lib\site-packages\IPython\core\pylabtools.py", line 121, in print_figure
fig.canvas.print_figure(bytes_io, **kw)

File "D:\Anaconda3\lib\site-packages\matplotlib\backend_bases.py", line 2208, in print_figure
**kwargs)

File "D:\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py", line 507, in print_png
FigureCanvasAgg.draw(self)

File "D:\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py", line 430, in draw
self.figure.draw(self.renderer)

File "D:\Anaconda3\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)

File "D:\Anaconda3\lib\site-packages\matplotlib\figure.py", line 1295, in draw
renderer, self, artists, self.suppressComposite)

File "D:\Anaconda3\lib\site-packages\matplotlib\image.py", line 138, in _draw_list_compositing_images
a.draw(renderer)

File "D:\Anaconda3\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)

File "D:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 2399, in draw
mimage._draw_list_compositing_images(renderer, self, artists)

File "D:\Anaconda3\lib\site-packages\matplotlib\image.py", line 138, in _draw_list_compositing_images
a.draw(renderer)

File "D:\Anaconda3\lib\site-packages\matplotlib\artist.py", line 55, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)

File "D:\Anaconda3\lib\site-packages\matplotlib\axis.py", line 1133, in draw
ticks_to_draw = self._update_ticks(renderer)

File "D:\Anaconda3\lib\site-packages\matplotlib\axis.py", line 974, in _update_ticks
tick_tups = list(self.iter_ticks())

File "D:\Anaconda3\lib\site-packages\matplotlib\axis.py", line 921, in iter_ticks
for i, val in enumerate(majorLocs)]

File "D:\Anaconda3\lib\site-packages\matplotlib\axis.py", line 921, in <listcomp>
for i, val in enumerate(majorLocs)]

File "D:\Anaconda3\lib\site-packages\matplotlib\dates.py", line 538, in __call__
dt = num2date(x, self.tz)

File "D:\Anaconda3\lib\site-packages\matplotlib\dates.py", line 441, in num2date
return _from_ordinalf(x, tz)

File "D:\Anaconda3\lib\site-packages\matplotlib\dates.py", line 256, in _from_ordinalf
dt = datetime.datetime.fromordinal(ix).replace(tzinfo=UTC)

OverflowError: Python int too large to convert to C long

<matplotlib.figure.Figure at 0xebecc88>

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.21.0
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: 0.9.6
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
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: None
pandas_gbq: None
pandas_datareader: None

workarounds

using

ax.plot(df.index.to_pydatetime(), df)

instead

ax.plot(df)

This problem does not occur prior to 0.21.0