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Module « numpy »

Fonction concatenate - module numpy

Signature de la fonction concatenate

Description

help(numpy.concatenate)

concatenate(
    (a1, a2, ...),
    axis=0,
    out=None,
    dtype=None,
    casting="same_kind"
)

Join a sequence of arrays along an existing axis.

Parameters
----------
a1, a2, ... : sequence of array_like
    The arrays must have the same shape, except in the dimension
    corresponding to `axis` (the first, by default).
axis : int, optional
    The axis along which the arrays will be joined.  If axis is None,
    arrays are flattened before use.  Default is 0.
out : ndarray, optional
    If provided, the destination to place the result. The shape must be
    correct, matching that of what concatenate would have returned if no
    out argument were specified.
dtype : str or dtype
    If provided, the destination array will have this dtype. Cannot be
    provided together with `out`.

    .. versionadded:: 1.20.0

casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
    Controls what kind of data casting may occur. Defaults to 'same_kind'.
    For a description of the options, please see :term:`casting`.

    .. versionadded:: 1.20.0

Returns
-------
res : ndarray
    The concatenated array.

See Also
--------
ma.concatenate : Concatenate function that preserves input masks.
array_split : Split an array into multiple sub-arrays of equal or
              near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
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).
stack : Stack a sequence of arrays along a new axis.
block : Assemble arrays from blocks.
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).
column_stack : Stack 1-D arrays as columns into a 2-D array.

Notes
-----
When one or more of the arrays to be concatenated is a MaskedArray,
this function will return a MaskedArray object instead of an ndarray,
but the input masks are *not* preserved. In cases where a MaskedArray
is expected as input, use the ma.concatenate function from the masked
array module instead.

Examples
--------
>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])

This function will not preserve masking of MaskedArray inputs.

>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data=[0, --, 2],
             mask=[False,  True, False],
       fill_value=999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data=[0, 1, 2, 2, 3, 4],
             mask=False,
       fill_value=999999)
>>> np.ma.concatenate([a, b])
masked_array(data=[0, --, 2, 2, 3, 4],
             mask=[False,  True, False, False, False, False],
       fill_value=999999)

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