Vous êtes un professionnel et vous avez besoin d'une formation ? Coder avec une
Intelligence Artificielle Voir le programme détaillé

Module « numpy »

Fonction trim_zeros - module numpy

Signature de la fonction trim_zeros

def trim_zeros(filt, trim='fb', axis=None) 

Description

help(numpy.trim_zeros)

Remove values along a dimension which are zero along all other.

Parameters
----------
filt : array_like
    Input array.
trim : {"fb", "f", "b"}, optional
    A string with 'f' representing trim from front and 'b' to trim from
    back. By default, zeros are trimmed on both sides.
    Front and back refer to the edges of a dimension, with "front" refering
    to the side with the lowest index 0, and "back" refering to the highest
    index (or index -1).
axis : int or sequence, optional
    If None, `filt` is cropped such, that the smallest bounding box is
    returned that still contains all values which are not zero.
    If an axis is specified, `filt` will be sliced in that dimension only
    on the sides specified by `trim`. The remaining area will be the
    smallest that still contains all values wich are not zero.

Returns
-------
trimmed : ndarray or sequence
    The result of trimming the input. The number of dimensions and the
    input data type are preserved.

Notes
-----
For all-zero arrays, the first axis is trimmed first.

Examples
--------
>>> import numpy as np
>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])

>>> np.trim_zeros(a, trim='b')
array([0, 0, 0, ..., 0, 2, 1])

Multiple dimensions are supported.

>>> b = np.array([[0, 0, 2, 3, 0, 0],
...               [0, 1, 0, 3, 0, 0],
...               [0, 0, 0, 0, 0, 0]])
>>> np.trim_zeros(b)
array([[0, 2, 3],
       [1, 0, 3]])

>>> np.trim_zeros(b, axis=-1)
array([[0, 2, 3],
       [1, 0, 3],
       [0, 0, 0]])

The input data type is preserved, list/tuple in means list/tuple out.

>>> np.trim_zeros([0, 1, 2, 0])
[1, 2]

Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé