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Module « numpy.matlib »

Fonction insert - module numpy.matlib

Signature de la fonction insert

def insert(arr, obj, values, axis=None) 

Description

help(numpy.matlib.insert)

Insert values along the given axis before the given indices.

Parameters
----------
arr : array_like
    Input array.
obj : slice, int, array-like of ints or bools
    Object that defines the index or indices before which `values` is
    inserted.

    .. versionchanged:: 2.1.2
        Boolean indices are now treated as a mask of elements to insert,
        rather than being cast to the integers 0 and 1.

    Support for multiple insertions when `obj` is a single scalar or a
    sequence with one element (similar to calling insert multiple
    times).
values : array_like
    Values to insert into `arr`. If the type of `values` is different
    from that of `arr`, `values` is converted to the type of `arr`.
    `values` should be shaped so that ``arr[...,obj,...] = values``
    is legal.
axis : int, optional
    Axis along which to insert `values`.  If `axis` is None then `arr`
    is flattened first.

Returns
-------
out : ndarray
    A copy of `arr` with `values` inserted.  Note that `insert`
    does not occur in-place: a new array is returned. If
    `axis` is None, `out` is a flattened array.

See Also
--------
append : Append elements at the end of an array.
concatenate : Join a sequence of arrays along an existing axis.
delete : Delete elements from an array.

Notes
-----
Note that for higher dimensional inserts ``obj=0`` behaves very different
from ``obj=[0]`` just like ``arr[:,0,:] = values`` is different from
``arr[:,[0],:] = values``. This is because of the difference between basic
and advanced :ref:`indexing <basics.indexing>`.

Examples
--------
>>> import numpy as np
>>> a = np.arange(6).reshape(3, 2)
>>> a
array([[0, 1],
       [2, 3],
       [4, 5]])
>>> np.insert(a, 1, 6)
array([0, 6, 1, 2, 3, 4, 5])
>>> np.insert(a, 1, 6, axis=1)
array([[0, 6, 1],
       [2, 6, 3],
       [4, 6, 5]])

Difference between sequence and scalars,
showing how ``obj=[1]`` behaves different from ``obj=1``:

>>> np.insert(a, [1], [[7],[8],[9]], axis=1)
array([[0, 7, 1],
       [2, 8, 3],
       [4, 9, 5]])
>>> np.insert(a, 1, [[7],[8],[9]], axis=1)
array([[0, 7, 8, 9, 1],
       [2, 7, 8, 9, 3],
       [4, 7, 8, 9, 5]])
>>> np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1),
...                np.insert(a, [1], [[7],[8],[9]], axis=1))
True

>>> b = a.flatten()
>>> b
array([0, 1, 2, 3, 4, 5])
>>> np.insert(b, [2, 2], [6, 7])
array([0, 1, 6, 7, 2, 3, 4, 5])

>>> np.insert(b, slice(2, 4), [7, 8])
array([0, 1, 7, 2, 8, 3, 4, 5])

>>> np.insert(b, [2, 2], [7.13, False]) # type casting
array([0, 1, 7, 0, 2, 3, 4, 5])

>>> x = np.arange(8).reshape(2, 4)
>>> idx = (1, 3)
>>> np.insert(x, idx, 999, axis=1)
array([[  0, 999,   1,   2, 999,   3],
       [  4, 999,   5,   6, 999,   7]])

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