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POSort on multiple NumPy arrays
 

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  1. POSort on multiple NumPy arrays
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    <p>I am creating a 2 dimensional numpy array that contains stock returns. I want to sum the return every 2 days, and if the sum is in the top two, I will set every element in a similar shaped array to True.</p> <p>For example, returns below is the daily returns for four different stocks.</p> <p><code> returns=np.array([<br> [0, 0, 4, 8],<br> [7, 5, 4, 1],<br> [10, 5, 7, 6],<br> [7, 5, 4, 2]]) </code></p> <p>For the first two days, columns 2 and 3 (using 0 based indexing) have the highest sums. For the second set of two days, columns 0 and 2 have the highest sums. The output array I want is</p> <p><code> bools=np.array([<br> [False, False, True, True],<br> [False, False, True, True],<br> [True, False, True, False],<br> [True, False, True, False]]) </code></p> <p>What are good ways to accomplish this?</p> <p>If there are ties with the sums of two days, I want to use another similarly shaped numpy array as tiebreakers.</p> <p>For example, if </p> <p><code> returns=np.array([<br> [0, 9, 4, 8],<br> [7, 5, 4, 0],<br> [10, 5, 7, 6],<br> [7, 5, 4, 2]]) </code></p> <p>For the first set of two days, columns 2 and 3 are tied for the second highest sum. I want to decide the tiebreaker by the greatest value in the last row for the tied columns so that the tie break between columns 2 and 3 look at tiebreaks[1][2] vs tiebreaks[1][3] (4 v 5), and that the ultimate output is bools2.</p> <p><code> tiebreaks=np.array([<br> [0, 0, 1, 1],<br> [2, 3, 4, 5],<br> [0, 5, 7, 6],<br> [-7, 5, -4, 2]]) </code></p> <p><code> bools2=np.array([<br> [False, True, False, True],<br> [False, True, False, True],<br> [True, False, True, False],<br> [True, False, True, False]]) </code></p> <p>Thanks for your help.</p>
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    1. USIdr
    1. USIdr
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      1. POSort on multiple NumPy arrays
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      1. VTUpMod
    1. COIn your example, things appear already sorted according to your criteria and the `names` array has an apparent unnecessary dimension. It would be helpful if you could provide a more realistic example and an example of the desired outcome.
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      1. POSort on multiple NumPy arrays
      1. USPaul
    2. COYour edit has made this a completely different question. Please roll back the edit and ask a new question instead.
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      1. POSort on multiple NumPy arrays
      1. USSven Marnach
 

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