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    <p>I prefer to use <strong><em>NP.where</em></strong> for indexing tasks of this sort (rather than <em>NP.ix_</em>)</p> <p>What is not mentioned in the OP is whether the result is selected by location (row/col in the source array) or by some condition (e.g., m >= 5). In any event, the code snippet below covers both scenarios.</p> <p>Three steps: </p> <ol> <li><p>create the <strong><em>condition array</em></strong>;</p></li> <li><p>generate an <strong><em>index array</em></strong> by calling <em>NP.where</em>, passing in this condition array; and</p></li> <li><p><em>apply</em> this index array against the source array</p></li> </ol> <hr> <pre><code>&gt;&gt;&gt; import numpy as NP &gt;&gt;&gt; cnd = (m==1) | (m==5) | (m==7) | (m==6) &gt;&gt;&gt; cnd matrix([[ True, False], [False, True], [ True, False], [ True, False]], dtype=bool) &gt;&gt;&gt; # generate the index array/matrix &gt;&gt;&gt; # by calling NP.where, passing in the condition (cnd) &gt;&gt;&gt; ndx = NP.where(cnd) &gt;&gt;&gt; ndx (matrix([[0, 1, 2, 3]]), matrix([[0, 1, 0, 0]])) &gt;&gt;&gt; # now apply it against the source array &gt;&gt;&gt; m[ndx] matrix([[1, 5, 7, 6]]) </code></pre> <p><br/> The argument passed to NP.where, <em>cnd</em>, is a boolean array, which in this case, is the result from a single expression comprised of compound conditional expressions (first line above)</p> <p>If constructing such a value filter doesn't apply to your particular use case, that's fine, you just need to generate the actual boolean matrix (the value of <strong><em>cnd</em></strong>) some other way (or create it directly).</p>
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