how do I calculate a rolling idxmax
<p>consider the <code>pd.Series</code> <code>s</code></p>

<pre><code>import pandas as pd
import numpy as np

np.random.seed([3,1415])
s = pd.Series(np.random.randint(0, 10, 10), list('abcdefghij'))
s

a    0
b    2
c    7
d    3
e    8
f    7
g    0
h    6
i    8
j    6
dtype: int64
</code></pre>

<p>I want to get the index for the max value for the rolling window of 3</p>

<pre><code>s.rolling(3).max()

a    NaN
b    NaN
c    7.0
d    7.0
e    8.0
f    8.0
g    8.0
h    7.0
i    8.0
j    8.0
dtype: float64
</code></pre>

<p>What I want is</p>

<pre><code>a    None
b    None
c       c
d       c
e       e
f       e
g       e
h       f
i       i
j       i
dtype: object
</code></pre>

<p>What I've done</p>

<pre><code>s.rolling(3).apply(np.argmax)

a    NaN
b    NaN
c    2.0
d    1.0
e    2.0
f    1.0
g    0.0
h    0.0
i    2.0
j    1.0
dtype: float64
</code></pre>

<p>which is obviously not what I want</p>

<p>There is no simple way to do that, because the argument that is passed to the rolling-applied function is a plain numpy array, not a pandas Series, so it doesn't know about the index.  Moreover, the rolling functions must return a float result, so they can't directly return the index values if they're not floats.</p>

<p>Here is one approach:</p>

<pre><code>&gt;&gt;&gt; s.index[s.rolling(3).apply(np.argmax)[2:].astype(int)+np.arange(len(s)-2)]
Index([u'c', u'c', u'e', u'e', u'e', u'f', u'i', u'i'], dtype='object')
</code></pre>

<p>The idea is to take the argmax values and align them with the series by adding a value indicating how far along in the series we are.  (That is, for the first argmax value we add zero, because it is giving us the index into a subsequence starting at index 0 in the original series; for the second argmax value we add one, because it is giving us the index into a subsequence starting at index 1 in the original series; etc.)</p>

<p>This gives the correct results, but doesn't include the two "None" values at the beginning; you'd have to add those back manually if you wanted them.</p>

<p>There is <a href="https://github.com/pandas-dev/pandas/issues/9481">an open pandas issue</a> to add rolling idxmax.</p>

python pandas numpy dataframe series