Is it possible to use argsort in descending order

Question

Consider the following code   avgDists   np array  1  8  6  9  4   ids   avgDists argsort    n    This gives me indices of the n smallest elements  Is it possible to use this same argsort in descending order to get the indices of n highest elements

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As  Kanmani hinted  an easier to interpret implementation may use numpy flip  as in the following   import numpy as np  avgDists   np array  1  8  6  9  4   ids   np flip np argsort avgDists   print ids    By using the visitor pattern rather than member functions  it is easier to read the order of operations

User · Answer

You can use the flip commands numpy flipud   or numpy fliplr   to get the indexes in descending order after sorting using the argsort command  Thats what I usually do

User · Answer

If you negate an array  the lowest elements become the highest elements and vice-versa   Therefore  the indices of the n highest elements are    -avgDists  argsort    n    Another way to reason about this  as mentioned in the comments  is to observe that the big elements are coming last in the argsort   So  you can read from the tail of the argsort to find the n highest elements    avgDists argsort     -1   n    Both methods are O n log n  in time complexity  because the argsort call is the dominant term here   But the second approach has a nice advantage  it replaces an O n  negation of the array with an O 1  slice   If you re working with small arrays inside loops then you may get some performance gains from avoiding that negation  and if you re working with huge arrays then you can save on memory usage because the negation creates a copy of the entire array     Note that these methods do not always give equivalent results  if a stable sort implementation is requested to argsort  e g  by passing the keyword argument kind  mergesort   then the first strategy will preserve the sorting stability  but the second strategy will break stability  i e  the positions of equal items will get reversed    Example timings   Using a small array of 100 floats and a length 30 tail  the view method was about 15  faster   gt  gt  gt  avgDists   np random rand 100   gt  gt  gt  n   30  gt  gt  gt  timeit  -avgDists  argsort    n  1 93   s    6 68 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   gt  gt  gt  timeit avgDists argsort     -1   n  1 64   s    3 39 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   gt  gt  gt  timeit avgDists argsort   -n     -1  1 64   s    3 66 ns per loop  mean    std  dev  of 7 runs  1000000 loops each    For larger arrays  the argsort is dominant and there is no significant timing difference   gt  gt  gt  avgDists   np random rand 1000   gt  gt  gt  n   300  gt  gt  gt  timeit  -avgDists  argsort    n  21 9   s    51 2 ns per loop  mean    std  dev  of 7 runs  10000 loops each   gt  gt  gt  timeit avgDists argsort     -1   n  21 7   s    33 3 ns per loop  mean    std  dev  of 7 runs  10000 loops each   gt  gt  gt  timeit avgDists argsort   -n     -1  21 9   s    37 1 ns per loop  mean    std  dev  of 7 runs  10000 loops each    Please note that the comment from nedim below is incorrect  Whether to truncate before or after reversing makes no difference in efficiency  since both of these operations are only striding a view of the array differently and not actually copying data

User · Answer

You could create a copy of the array and then multiply each element with -1  As an effect the before largest elements would become the smallest  The indeces of the n smallest elements in the copy are the n greatest elements in the original

User · Answer

An elegant way could be as follows - ids   np flip np argsort avgDists    This will give you indices of elements sorted in descending order  Now you can use regular slicing    top n   ids  n

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Another way is to use only a  -  in the argument for argsort as in     df np argsort -df    0      provided df is the dataframe and you want to sort it by the first column  represented by the column number  0     Change the column-name as appropriate   Of course  the column has to be a numeric one

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Instead of using np argsort you could use np argpartition - if you only need the indices of the lowest highest n elements    That doesn t require to sort the whole array but just the part that you need but note that the  order inside your partition  is undefined  so while it gives the correct indices they might not be correctly ordered    gt  gt  gt  avgDists    1  8  6  9  4   gt  gt  gt  np array avgDists  argpartition 2   2     indices of lowest 2 items array  0  4   dtype int64    gt  gt  gt  np array avgDists  argpartition -2  -2      indices of highest 2 items array  1  3   dtype int64

User · Answer

With your example       avgDists   np array  1  8  6  9  4     Obtain indexes of n maximal values   ids   np argpartition avgDists  -n  -n     Sort them in descending order   ids   ids np argsort avgDists ids     -1     Obtain results  for n 4     gt  gt  gt  avgDists ids  array  9  8  6  4

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Just like Python  in that    -1  reverses the array returned by argsort   and   n  gives that last n elements    gt  gt  gt  avgDists np array  1  8  6  9  4    gt  gt  gt  n 3  gt  gt  gt  ids   avgDists argsort     -1   n   gt  gt  gt  ids array  3  1  2     The advantage of this method is that ids is a view of avgDists    gt  gt  gt  ids flags   C CONTIGUOUS   False   F CONTIGUOUS   False   OWNDATA   False   WRITEABLE   True   ALIGNED   True   UPDATEIFCOPY   False    The  OWNDATA  being False indicates this is a view  not a copy    Another way to do this is something like    -avgDists  argsort    n    The problem is that the way this works is to create negative of each element in the array    gt  gt  gt   -avgDists  array  -1  -8  -6  -9  -4     ANd creates a copy to do so    gt  gt  gt   -avgDists n  flags  OWNDATA   True   So if you time each  with this very small data set    gt  gt  gt  import timeit  gt  gt  gt  timeit timeit   -avgDists  argsort    3    setup  from   main   import avgDists   4 2879798610229045  gt  gt  gt  timeit timeit  avgDists argsort     -1   3    setup  from   main   import avgDists   2 8372560259886086   The view method is substantially faster  and uses 1 2 the memory

[python] Is it possible to use argsort in descending order?

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