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# numpy.lexsort

`numpy.``lexsort`(keys, axis=-1)

  Perform an indirect sort using a sequence of keys.

  Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D array is provided for the keys argument, it’s rows are interpreted as the sorting keys and sorting is according to the last row, second last row etc.

  Parameters : | keys : (k,N) array or tuple containing k (N,)-shaped sequences The k different “columns” to be sorted. The last column (or row if keys is a 2D array) is the primary sort key. axis : int, optional Axis to be indirectly sorted. By default, sort over the last axis.
  Returns : | indices : (N,) ndarray of ints Array of indices that sort the keys along the specified axis.

  See also

  `argsort`
    Indirect sort.
  `ndarray.sort`
    In-place sort.
  `sort`
    Return a sorted copy of an array.

  Examples

  Sort names: first by surname, then by name.

      >>> surnames =    ('Hertz',    'Galilei', 'Hertz')
      >>> first_names = ('Heinrich', 'Galileo', 'Gustav')
      >>> ind = np.lexsort((first_names, surnames))
      >>> ind
      array([1, 2, 0])

      >>> [surnames[i] + ", " + first_names[i] for i in ind]
      ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']

  Sort two columns of numbers:

      >>> a = [1,5,1,4,3,4,4] # First column
      >>> b = [9,4,0,4,0,2,1] # Second column
      >>> ind = np.lexsort((b,a)) # Sort by a, then by b
      >>> print ind
      [2 0 4 6 5 3 1]

      >>> [(a[i],b[i]) for i in ind]
      [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]

  Note that sorting is first according to the elements of `a`. Secondary sorting is according to the elements of `b`.

  A normal `argsort` would have yielded:

      >>> [(a[i],b[i]) for i in np.argsort(a)]
      [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]

  Structured arrays are sorted lexically by `argsort`:

      >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
      ...              dtype=np.dtype([('x', int), ('y', int)]))

      >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
      array([2, 0, 4, 6, 5, 3, 1])

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