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Fonction lexsort - module numpy

Signature de la fonction lexsort

Description

lexsort.__doc__

    lexsort(keys, axis=-1)

    Perform an indirect stable 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, its 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
    >>> ind
    array([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])