numpy.concatenate((a1, a2, ...), axis=0)
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 : int, optional The axis along which the arrays will be joined. Default is 0. |
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Returns: |
res : ndarray The concatenated array. |
See also
ma.concatenate
array_split
split
hsplit
vsplit
dsplit
stack
hstack
vstack
dstack
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.
>>> 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]])
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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https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.concatenate.html