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POReplacing column values in an array using putmask and preserving order
 

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    <p>I have a numpy array of floats and I wish to recalculate new values using a formula that depends on the column being recalculated. </p> <p>I have initially tried to loop over the columns, masking the array except the column to be recalculated, and the replacing the values with the new ones with numpy.putmask but this does not retain the order, as it attempts to place a value in each element and failing that tries with the next calculated value on the next element, à la:</p> <pre><code>&gt;&gt;&gt; import numpy as np &gt;&gt;&gt; x = [[ 1., 2.], [ 3., 4.], [ 5., 6.], [ 7., 8.], [ 9., 10.]] &gt;&gt;&gt; mask = [[ True, False], [ True, False], [ True, False], [ True, False], [ True, False]] &gt;&gt;&gt; y = [ 21., 22., 23., 24., 25.] &gt;&gt;&gt; np.putmask(x,mask,y) &gt;&gt;&gt; print x [[ 21. 2.] [ 23. 4.] [ 25. 6.] [ 22. 8.] [ 24. 10.]] </code></pre> <p>I need a solution that will retry with the same value until it finds a True value, such that x would look like:</p> <pre><code>[[ 21. 2.] [ 22. 4.] [ 23. 6.] [ 24. 8.] [ 25. 10.]] </code></pre> <p>Any solutions or other methods welcomed. Thanks.</p>
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