Issue 1:
When value_counts on different list value, it's ok:

ser1 = pd.Series([[1], [2], [2]])
ser1.value_counts()

[2] 2
[1] 1
dtype: int64

But, when value_counts on unique list value, it's failed:

ser2 = pd.Series([[1], [1], [1]])
ser2.value_counts()

---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-64-845402fb8b57> in <module>()
1 ser2 = pd.Series([[1], [1], [1]])
----> 2 ser2.value_counts()

/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/base.pyc in value_counts(self, normalize, sort, ascending, bins, dropna)
945 from pandas.core.algorithms import value_counts
946 result = value_counts(self, sort=sort, ascending=ascending,
--> 947 normalize=normalize, bins=bins, dropna=dropna)
948 return result
949

/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/algorithms.pyc in value_counts(values, sort, ascending, normalize, bins, dropna)
276 else:
277 # ndarray path. pass original to handle DatetimeTzBlock
--> 278 keys, counts = _value_counts_arraylike(values, dropna=dropna)
279
280 from pandas import Index, Series

/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/algorithms.pyc in _value_counts_arraylike(values, dropna)
346 values = com._ensure_object(values)
347 mask = com.isnull(values)
--> 348 keys, counts = htable.value_count_object(values, mask)
349 if not dropna and mask.any():
350 keys = np.insert(keys, 0, np.NaN)

pandas/hashtable.pyx in pandas.hashtable.value_count_object (pandas/hashtable.c:18970)()

pandas/hashtable.pyx in pandas.hashtable.value_count_object (pandas/hashtable.c:18705)()

TypeError: unhashable type: 'list'

I tried 1.18.1 and 1.19.0

Output of pd.show_versions(), 1.18.1

INSTALLED VERSIONS

commit: None

python: 2.7.12.final.0

python-bits: 64

OS: Linux

OS-release: 3.10.0-327.36.2.el7.x86_64

machine: x86_64

processor: x86_64

byteorder: little

LC_ALL: None

LANG: en_US.UTF-8

pandas: 0.18.1

nose: 1.3.7

pip: 8.1.2

setuptools: 27.2.0

Cython: 0.24.1

numpy: 1.11.1

scipy: 0.18.1

statsmodels: 0.6.1

xarray: None

IPython: 5.1.0

sphinx: 1.4.6

patsy: 0.4.1

dateutil: 2.5.3

pytz: 2016.6.1

blosc: None

bottleneck: 1.1.0

tables: 3.2.3.1

numexpr: 2.6.1

matplotlib: 1.5.3

openpyxl: 2.3.2

xlrd: 1.0.0

xlwt: 1.1.2

xlsxwriter: 0.9.3

lxml: 3.6.4

bs4: 4.5.1

html5lib: None

httplib2: None

apiclient: None

sqlalchemy: 1.0.13

pymysql: None

psycopg2: None

jinja2: 2.8

boto: 2.42.0

pandas_datareader: None

Output of pd.show_versions(), 1.19.0

INSTALLED VERSIONS

commit: None

python: 2.7.12.final.0

python-bits: 64

OS: Linux

OS-release: 4.4.0-45-generic

machine: x86_64

processor: x86_64

byteorder: little

LC_ALL: None

LANG: None

LOCALE: None.None

pandas: 0.19.0

nose: 1.3.7

pip: 8.1.2

setuptools: 27.2.0

Cython: 0.24.1

numpy: 1.11.2

scipy: 0.18.1

statsmodels: 0.6.1

xarray: None

IPython: 5.1.0

sphinx: 1.4.6

patsy: 0.4.1

dateutil: 2.5.3

pytz: 2016.7

blosc: None

bottleneck: 1.1.0

tables: 3.2.3.1

numexpr: 2.6.1

matplotlib: 1.5.3

openpyxl: 2.3.2

xlrd: 1.0.0

xlwt: 1.1.2

xlsxwriter: 0.9.3

lxml: 3.6.4

bs4: 4.5.1

html5lib: None

httplib2: None

apiclient: None

sqlalchemy: 1.0.13

pymysql: None

psycopg2: None

jinja2: 2.8

boto: 2.42.0

pandas_datareader: None

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

import pandas as pd

df = pd.DataFrame({'foo': [1.0001] * 5 + [0.]*5})

df

foo

0
1.0001

1
1.0001

2
1.0001

3
1.0001

4
1.0001

5
0.0000

6
0.0000

7
0.0000

8
0.0000

9
0.0000

df.rolling(2).sum()

foo

0
NaN

1
2.000200e+00

2
2.000200e+00

3
2.000200e+00

4
2.000200e+00

5
1.000100e+00

6
4.440892e-16

7
4.440892e-16

8
4.440892e-16

9
4.440892e-16

Problem description

Rolling sum of zeros sometimes gives a very small negative number, which is not the desired behavior.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None

python: 3.5.4.final.0

python-bits: 64

OS: Linux

OS-release: 4.1.35-pv-ts2

machine: x86_64

processor:

byteorder: little

LC_ALL: en_US.utf8

LANG: en_US.utf8

LOCALE: en_US.UTF-8

pandas: 0.20.3

pytest: 3.2.1

pip: 9.0.1

setuptools: 36.4.0

Cython: 0.26

numpy: 1.12.1

scipy: 0.19.1

xarray: None

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: 2.0.2

openpyxl: 2.4.8

xlrd: 1.1.0

xlwt: None

xlsxwriter: 0.9.8

lxml: None

bs4: 4.6.0

html5lib: 0.9999999

sqlalchemy: 1.1.13

pymysql: None

psycopg2: None

jinja2: 2.9.6

s3fs: None

pandas_gbq: None

pandas_datareader: None