Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les fondamentaux Voir le programme détaillé

Module « numpy »

Fonction split - module numpy

Signature de la fonction split

def split(ary, indices_or_sections, axis=0) 

Description

help(numpy.split)

Split an array into multiple sub-arrays as views into `ary`.

Parameters
----------
ary : ndarray
    Array to be divided into sub-arrays.
indices_or_sections : int or 1-D array
    If `indices_or_sections` is an integer, N, the array will be divided
    into N equal arrays along `axis`.  If such a split is not possible,
    an error is raised.

    If `indices_or_sections` is a 1-D array of sorted integers, the entries
    indicate where along `axis` the array is split.  For example,
    ``[2, 3]`` would, for ``axis=0``, result in

    - ary[:2]
    - ary[2:3]
    - ary[3:]

    If an index exceeds the dimension of the array along `axis`,
    an empty sub-array is returned correspondingly.
axis : int, optional
    The axis along which to split, default is 0.

Returns
-------
sub-arrays : list of ndarrays
    A list of sub-arrays as views into `ary`.

Raises
------
ValueError
    If `indices_or_sections` is given as an integer, but
    a split does not result in equal division.

See Also
--------
array_split : Split an array into multiple sub-arrays of equal or
              near-equal size.  Does not raise an exception if
              an equal division cannot be made.
hsplit : Split array into multiple sub-arrays horizontally (column-wise).
vsplit : Split array into multiple sub-arrays vertically (row wise).
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
concatenate : Join a sequence of arrays along an existing axis.
stack : Join a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise).
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third dimension).

Examples
--------
>>> import numpy as np
>>> x = np.arange(9.0)
>>> np.split(x, 3)
[array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]

>>> x = np.arange(8.0)
>>> np.split(x, [3, 5, 6, 10])
[array([0.,  1.,  2.]),
 array([3.,  4.]),
 array([5.]),
 array([6.,  7.]),
 array([], dtype=float64)]

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