numpy.hstack
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numpy.hstack(tup)
[source] -
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by
hsplit
.This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions
concatenate
,stack
andblock
provide more general stacking and concatenation operations.Parameters: -
tup : sequence of ndarrays
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The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
Returns: -
stacked : ndarray
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The array formed by stacking the given arrays.
See also
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stack
- Join a sequence of arrays along a new axis.
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vstack
- Stack arrays in sequence vertically (row wise).
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dstack
- Stack arrays in sequence depth wise (along third axis).
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concatenate
- Join a sequence of arrays along an existing axis.
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hsplit
- Split array along second axis.
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block
- Assemble arrays from blocks.
Examples
>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]])
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.hstack.html