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copied!<p>If you're looking for other interesting ways to solve the problem, here is an answer using <em>itertools</em>:</p> <pre><code>import datetime as dt from collections import deque from itertools import tee, islice, izip def dayiter(start, end): one = dt.timedelta(days=1) day = start while day <= end: yield day day += one def moving_average(mapping, window, dft=0): n = float(window) t1, t2 = tee(dayiter(min(mapping), max(mapping))) s = sum(mapping.get(day, dft) for day in islice(t2, window)) yield s / n for olddate, newdate in izip(t1, t2): oldvalue = mapping.get(olddate, dft) newvalue = mapping.get(newdate, dft) s += newvalue - oldvalue yield s / n example = {dt.datetime(2008, 1, 1) : 5, dt.datetime(2008, 1, 2) : 6, dt.datetime(2008, 1, 3) : 7, dt.datetime(2008, 1, 4) : 9, dt.datetime(2008, 1, 5) : 12, dt.datetime(2008, 1, 6) : 15, dt.datetime(2008, 1, 7) : 20, dt.datetime(2008, 1, 8) : 22, dt.datetime(2008, 1, 9) : 25, dt.datetime(2008, 1, 10) : 35} for ma in moving_average(example, window=3): print ma </code></pre> <p>The ideas involved are:</p> <ul> <li><p>Use a simple generator to make a date iterator that loops over consecutive days from the lowest to the highest.</p></li> <li><p>Use <em>itertools.tee</em> to construct a pair of iterators over the oldest data and the newest data (the front of the data window and the back).</p></li> <li><p>Keep a running sum in a variable <em>s</em>. On each iteration, update <em>s</em> by subtracting the oldest value and adding the newest value.</p></li> <li><p>This solution is space efficient (it keeps no more than <em>window</em> values in memory) and it is time efficient, one addition and one subtraction for each day regardless of the size of the window.</p></li> <li><p>Handle missing days by defaulting to zero. There are other strategies that could be used for missing days (like using the current moving average as a default or adjusting <em>n</em> up and down to reflect the number of actual data points in the window). </p></li> </ul>

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