<p dir="auto">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.</p>
<h4 dir="auto">Code Sample, a copy-pastable example if possible</h4>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="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]})"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span>
<span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span>
<span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">DataFrame</span>({<span class="pl-s">'task_complete'</span>:[<span class="pl-s">'success'</span>,<span class="pl-s">'success'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'success'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'success'</span>],
    <span class="pl-s">'value'</span>:[<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-c1">4.5</span>,<span class="pl-c1">5.7</span>,<span class="pl-c1">3.0</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-c1">6.7</span>,<span class="pl-c1">3.78</span>]})</pre></div>
<p dir="auto"><code class="notranslate">df.value.describe()</code> returns a RuntimeWarning from numpy, which then gives this unexpected result for the quantiles:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><span class="pl-v">In</span> [<span class="pl-c1">5</span>]: <span class="pl-s1">df</span>.<span class="pl-s1">value</span>.<span class="pl-en">describe</span>()
<span class="pl-c1">/</span><span class="pl-v">Users</span><span class="pl-c1">/</span><span class="pl-s1">adrianpalacios</span><span class="pl-c1">/</span><span class="pl-s1">anaconda</span><span class="pl-c1">/</span><span class="pl-s1">lib</span><span class="pl-c1">/</span><span class="pl-s1">python3</span>.<span class="pl-c1">4</span><span class="pl-c1">/</span><span class="pl-s1">site</span><span class="pl-c1">-</span><span class="pl-s1">packages</span><span class="pl-c1">/</span><span class="pl-s1">numpy</span><span class="pl-c1">/</span><span class="pl-s1">lib</span><span class="pl-c1">/</span><span class="pl-s1">function_base</span>.<span class="pl-s1">py</span>:<span class="pl-c1">3403</span>: <span class="pl-v">RuntimeWarning</span>: <span class="pl-v">Invalid</span> <span class="pl-s1">value</span> <span class="pl-s1">encountered</span> <span class="pl-c1">in</span> <span class="pl-s1">median</span>
  <span class="pl-v">RuntimeWarning</span>)
<span class="pl-v">Out</span>[<span class="pl-c1">5</span>]:
<span class="pl-s1">count</span>    <span class="pl-c1">5.000000</span>
<span class="pl-s1">mean</span>     <span class="pl-c1">4.736000</span>
<span class="pl-s1">std</span>      <span class="pl-c1">1.480703</span>
<span class="pl-s1">min</span>      <span class="pl-c1">3.000000</span>
<span class="pl-c1">25</span><span class="pl-c1">%</span>           <span class="pl-v">NaN</span>
<span class="pl-c1">50</span><span class="pl-c1">%</span>           <span class="pl-v">NaN</span>
<span class="pl-c1">75</span><span class="pl-c1">%</span>           <span class="pl-v">NaN</span>
<span class="pl-s1">max</span>      <span class="pl-c1">6.700000</span>
<span class="pl-v">Name</span>: <span class="pl-s1">value</span>, <span class="pl-s1">dtype</span>: <span class="pl-s1">float64</span></pre></div>
<h4 dir="auto">Expected Output</h4>
<p dir="auto">I got this using a different conda environment that has not been upgraded to latest pandas version:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><span class="pl-v">In</span> [<span class="pl-c1">5</span>]: <span class="pl-s1">df</span>.<span class="pl-s1">value</span>.<span class="pl-en">describe</span>()
<span class="pl-v">Out</span>[<span class="pl-c1">5</span>]:
<span class="pl-s1">count</span>    <span class="pl-c1">5.000000</span>
<span class="pl-s1">mean</span>     <span class="pl-c1">4.736000</span>
<span class="pl-s1">std</span>      <span class="pl-c1">1.480703</span>
<span class="pl-s1">min</span>      <span class="pl-c1">3.000000</span>
<span class="pl-c1">25</span><span class="pl-c1">%</span>      <span class="pl-c1">3.780000</span>
<span class="pl-c1">50</span><span class="pl-c1">%</span>      <span class="pl-c1">4.500000</span>
<span class="pl-c1">75</span><span class="pl-c1">%</span>      <span class="pl-c1">5.700000</span>
<span class="pl-s1">max</span>      <span class="pl-c1">6.700000</span>
<span class="pl-v">Name</span>: <span class="pl-s1">value</span>, <span class="pl-s1">dtype</span>: <span class="pl-s1">float64</span></pre></div>
<p dir="auto">Dropping the NaN's works in pandas 18.1:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><span class="pl-v">In</span> [<span class="pl-c1">9</span>]: <span class="pl-s1">df</span>.<span class="pl-s1">value</span>.<span class="pl-en">dropna</span>().<span class="pl-en">describe</span>()
<span class="pl-v">Out</span>[<span class="pl-c1">9</span>]:
<span class="pl-s1">count</span>    <span class="pl-c1">5.000000</span>
<span class="pl-s1">mean</span>     <span class="pl-c1">4.736000</span>
<span class="pl-s1">std</span>      <span class="pl-c1">1.480703</span>
<span class="pl-s1">min</span>      <span class="pl-c1">3.000000</span>
<span class="pl-c1">25</span><span class="pl-c1">%</span>      <span class="pl-c1">3.780000</span>
<span class="pl-c1">50</span><span class="pl-c1">%</span>      <span class="pl-c1">4.500000</span>
<span class="pl-c1">75</span><span class="pl-c1">%</span>      <span class="pl-c1">5.700000</span>
<span class="pl-s1">max</span>      <span class="pl-c1">6.700000</span>
<span class="pl-v">Name</span>: <span class="pl-s1">value</span>, <span class="pl-s1">dtype</span>: <span class="pl-s1">float64</span></pre></div>
<p dir="auto">However, this work-around is not a great option when multiple columns w/ NaNs are present:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="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"><pre class="notranslate"><span class="pl-s1">df2</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">DataFrame</span>({<span class="pl-s">'task_complete'</span>:[<span class="pl-s">'success'</span>,<span class="pl-s">'success'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'success'</span>,<span class="pl-s">'fail'</span>,<span class="pl-s">'success'</span>],
    <span class="pl-s">'value'</span>:[<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-c1">4.5</span>,<span class="pl-c1">5.7</span>,<span class="pl-c1">3.0</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-c1">6.7</span>,<span class="pl-c1">3.78</span>],
    <span class="pl-s">'more_values'</span>:[<span class="pl-c1">8.2</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-c1">9.4</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>,<span class="pl-s1">np</span>.<span class="pl-s1">nan</span>]
})

<span class="pl-v">In</span> [<span class="pl-c1">17</span>]: <span class="pl-s1">df2</span>[[<span class="pl-s">'value'</span>,<span class="pl-s">'more_values'</span>]].<span class="pl-en">dropna</span>().<span class="pl-en">describe</span>()
<span class="pl-v">Out</span>[<span class="pl-c1">17</span>]:
       <span class="pl-s1">value</span>  <span class="pl-s1">more_values</span>
<span class="pl-s1">count</span>    <span class="pl-c1">0.0</span>          <span class="pl-c1">0.0</span>
<span class="pl-s1">mean</span>     <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-s1">std</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-s1">min</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-c1">25</span><span class="pl-c1">%</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-c1">50</span><span class="pl-c1">%</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-c1">75</span><span class="pl-c1">%</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span>
<span class="pl-s1">max</span>      <span class="pl-v">NaN</span>          <span class="pl-v">NaN</span></pre></div>
<h4 dir="auto">output of <code class="notranslate">pd.show_versions()</code></h4>
<h2 dir="auto">INSTALLED VERSIONS</h2>
<p dir="auto">commit: None<br>
python: 3.4.4.final.0<br>
python-bits: 64<br>
OS: Darwin<br>
OS-release: 15.4.0<br>
machine: x86_64<br>
processor: i386<br>
byteorder: little<br>
LC_ALL: None<br>
LANG: en_US.UTF-8</p>
<p dir="auto">pandas: 0.18.1<br>
nose: 1.3.7<br>
pip: 8.1.1<br>
setuptools: 20.2.2<br>
Cython: 0.22.1<br>
numpy: 1.10.4<br>
scipy: 0.17.0<br>
statsmodels: 0.6.1<br>
xarray: None<br>
IPython: 4.2.0<br>
sphinx: 1.3.1<br>
patsy: 0.3.0<br>
dateutil: 2.4.2<br>
pytz: 2015.7<br>
blosc: None<br>
bottleneck: 1.0.0<br>
tables: 3.2.0<br>
numexpr: 2.5<br>
matplotlib: 1.5.1<br>
openpyxl: 1.8.5<br>
xlrd: 0.9.3<br>
xlwt: 1.0.0<br>
xlsxwriter: 0.7.3<br>
lxml: 3.4.4<br>
bs4: 4.3.2<br>
html5lib: None<br>
httplib2: 0.9.1<br>
apiclient: None<br>
sqlalchemy: 1.0.5<br>
pymysql: None<br>
psycopg2: None<br>
jinja2: 2.8<br>
boto: 2.38.0<br>
pandas_datareader: None</p>
<h4 dir="auto">Code Sample, a copy-pastable example if possible</h4>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import pandas as pd
import numpy
s = pd.Series([1, 2, 3, 4, numpy.nan])
s.quantile(0.5)
nan"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span>
<span class="pl-k">import</span> <span class="pl-s1">numpy</span>
<span class="pl-s1">s</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-v">Series</span>([<span class="pl-c1">1</span>, <span class="pl-c1">2</span>, <span class="pl-c1">3</span>, <span class="pl-c1">4</span>, <span class="pl-s1">numpy</span>.<span class="pl-s1">nan</span>])
<span class="pl-s1">s</span>.<span class="pl-en">quantile</span>(<span class="pl-c1">0.5</span>)
<span class="pl-s1">nan</span></pre></div>
<h4 dir="auto">Expected Output</h4>
<p dir="auto">I would expect 2.5 as output (as with version 0.17.1).</p>
<h4 dir="auto">output of <code class="notranslate">pd.show_versions()</code></h4>
<p dir="auto">commit: None<br>
python: 2.7.6.final.0<br>
python-bits: 64<br>
OS: Linux<br>
OS-release: 3.13.0-85-generic<br>
machine: x86_64<br>
processor: x86_64<br>
byteorder: little<br>
LC_ALL: None<br>
LANG: de_DE.UTF-8</p>
<p dir="auto">pandas: 0.18.1<br>
nose: None<br>
pip: 1.5.4<br>
setuptools: 2.2<br>
Cython: None<br>
numpy: 1.11.0<br>
scipy: 0.16.1<br>
statsmodels: None<br>
xarray: None<br>
IPython: None<br>
sphinx: None<br>
patsy: None<br>
dateutil: 2.5.3<br>
pytz: 2016.4<br>
blosc: None<br>
bottleneck: None<br>
tables: None<br>
numexpr: None<br>
matplotlib: 1.5.1<br>
openpyxl: 2.3.2<br>
xlrd: 0.9.4<br>
xlwt: None<br>
xlsxwriter: None<br>
lxml: None<br>
bs4: None<br>
html5lib: None<br>
httplib2: None<br>
apiclient: None<br>
sqlalchemy: None<br>
pymysql: None<br>
psycopg2: None<br>
jinja2: None<br>
boto: None<br>
pandas_datareader: None</p>