Issue 1:
I just upgraded to 18.1 w/ conda. I started noticing this problem in some notebooks I created before the upgrade but recently revisited for further analysis.

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd
df = pd.DataFrame({'task_complete':['success','success','fail','fail','success','fail','success'],
'value':[np.nan,4.5,5.7,3.0,np.nan,6.7,3.78]})

df.value.describe() returns a RuntimeWarning from numpy, which then gives this unexpected result for the quantiles:

In [5]: df.value.describe()
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/numpy/lib/function_base.py:3403: RuntimeWarning: Invalid value encountered in median
RuntimeWarning)
Out[5]:
count 5.000000
mean 4.736000
std 1.480703
min 3.000000
25% NaN
50% NaN
75% NaN
max 6.700000
Name: value, dtype: float64

Expected Output

I got this using a different conda environment that has not been upgraded to latest pandas version:

In [5]: df.value.describe()
Out[5]:
count 5.000000
mean 4.736000
std 1.480703
min 3.000000
25% 3.780000
50% 4.500000
75% 5.700000
max 6.700000
Name: value, dtype: float64

Dropping the NaN's works in pandas 18.1:

In [9]: df.value.dropna().describe()
Out[9]:
count 5.000000
mean 4.736000
std 1.480703
min 3.000000
25% 3.780000
50% 4.500000
75% 5.700000
max 6.700000
Name: value, dtype: float64

However, this work-around is not a great option when multiple columns w/ NaNs are present:

df2 = pd.DataFrame({'task_complete':['success','success','fail','fail','success','fail','success'],
'value':[np.nan,4.5,5.7,3.0,np.nan,6.7,3.78],
'more_values':[8.2,np.nan,np.nan,np.nan,9.4,np.nan,np.nan]
})

In [17]: df2[['value','more_values']].dropna().describe()
Out[17]:
value more_values
count 0.0 0.0
mean NaN NaN
std NaN NaN
min NaN NaN
25% NaN NaN
50% NaN NaN
75% NaN NaN
max NaN NaN

output of pd.show_versions()

INSTALLED VERSIONS

commit: None

python: 3.4.4.final.0

python-bits: 64

OS: Darwin

OS-release: 15.4.0

machine: x86_64

processor: i386

byteorder: little

LC_ALL: None

LANG: en_US.UTF-8

pandas: 0.18.1

nose: 1.3.7

pip: 8.1.1

setuptools: 20.2.2

Cython: 0.22.1

numpy: 1.10.4

scipy: 0.17.0

statsmodels: 0.6.1

xarray: None

IPython: 4.2.0

sphinx: 1.3.1

patsy: 0.3.0

dateutil: 2.4.2

pytz: 2015.7

blosc: None

bottleneck: 1.0.0

tables: 3.2.0

numexpr: 2.5

matplotlib: 1.5.1

openpyxl: 1.8.5

xlrd: 0.9.3

xlwt: 1.0.0

xlsxwriter: 0.7.3

lxml: 3.4.4

bs4: 4.3.2

html5lib: None

httplib2: 0.9.1

apiclient: None

sqlalchemy: 1.0.5

pymysql: None

psycopg2: None

jinja2: 2.8

boto: 2.38.0

pandas_datareader: None

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

import pandas as pd
import numpy
s = pd.Series([1, 2, 3, 4, numpy.nan])
s.quantile(0.5)
nan

Expected Output

I would expect 2.5 as output (as with version 0.17.1).

output of pd.show_versions()

commit: None

python: 2.7.6.final.0

python-bits: 64

OS: Linux

OS-release: 3.13.0-85-generic

machine: x86_64

processor: x86_64

byteorder: little

LC_ALL: None

LANG: de_DE.UTF-8

pandas: 0.18.1

nose: None

pip: 1.5.4

setuptools: 2.2

Cython: None

numpy: 1.11.0

scipy: 0.16.1

statsmodels: None

xarray: None

IPython: None

sphinx: None

patsy: None

dateutil: 2.5.3

pytz: 2016.4

blosc: None

bottleneck: None

tables: None

numexpr: None

matplotlib: 1.5.1

openpyxl: 2.3.2

xlrd: 0.9.4

xlwt: None

xlsxwriter: None

lxml: None

bs4: None

html5lib: None

httplib2: None

apiclient: None

sqlalchemy: None

pymysql: None

psycopg2: None

jinja2: None

boto: None

pandas_datareader: None