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POProduct of array elements by group in numpy (Python)
 

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  1. POProduct of array elements by group in numpy (Python)
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    <p>I'm trying to build a function that returns the products of subsets of array elements. Basically I want to build a <code>prod_by_group</code> function that does this:</p> <pre><code>values = np.array([1, 2, 3, 4, 5, 6]) groups = np.array([1, 1, 1, 2, 3, 3]) Vprods = prod_by_group(values, groups) </code></pre> <p>And the resulting <code>Vprods</code> should be:</p> <pre><code>Vprods array([6, 4, 30]) </code></pre> <p>There's a great answer here for sums of elements that I think it should be similar to: <a href="https://stackoverflow.com/a/4387453/1085691">https://stackoverflow.com/a/4387453/1085691</a></p> <p>I tried taking the <code>log</code> first, then <code>sum_by_group</code>, then <code>exp</code>, but ran into numerical issues.</p> <p>There are some other similar answers here for min and max of elements by group: <a href="https://stackoverflow.com/a/8623168/1085691">https://stackoverflow.com/a/8623168/1085691</a></p> <p>Edit: Thanks for the quick answers! I'm trying them out. I should add that I want it to be as fast as possible (that's the reason I'm trying to get it in numpy in some vectorized way, like the examples I gave).</p> <p>Edit: I evaluated all the answers given so far, and the best one is given by @seberg below. Here's the full function that I ended up using:</p> <pre><code>def prod_by_group(values, groups): order = np.argsort(groups) groups = groups[order] values = values[order] group_changes = np.concatenate(([0], np.where(groups[:-1] != groups[1:])[0] + 1)) return np.multiply.reduceat(values, group_changes) </code></pre>
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    1. COYou might want to look at [pandas](http://pandas.pydata.org/), which is built on Numpy and provides very useful functions for grouping data and computing aggregate functions over the groups.
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    2. CO@BrenBarn that's not particularly helpful, we could at least narrow it down to a function that might be similar to this case.
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    3. COThe functions are called `group_by` and `aggregate`, but my point is that if you want to do this a lot, it pays to read the `pandas` documentation and learn to use pandas as a whole, because it makes this kind of thing easy with its entire setup of data structures.
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