vstack(tup)

This is equivalent to concatenation along the first axis after 1-D arrays of shape :None:None:`(N,)` have been reshaped to :None:None:`(1,N)`. Rebuilds arrays divided by vsplit .

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 and block provide more general stacking and concatenation operations.

Parameters

tup : sequence of ndarrays

The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.

Returns

stacked : ndarray

The array formed by stacking the given arrays, will be at least 2-D.

Stack arrays in sequence vertically (row wise).

See Also

block

Assemble an nd-array from nested lists of blocks.

column_stack

Stack 1-D arrays as columns into a 2-D array.

concatenate

Join a sequence of arrays along an existing axis.

dstack

Stack arrays in sequence depth wise (along third axis).

hstack

Stack arrays in sequence horizontally (column wise).

stack

Join a sequence of arrays along a new axis.

vsplit

Split an array into multiple sub-arrays vertically (row-wise).

Examples

>>> a = np.array([1, 2, 3])
... b = np.array([4, 5, 6])
... np.vstack((a,b))
array([[1, 2, 3],
       [4, 5, 6]])

>>> a = np.array([[1], [2], [3]])
... b = np.array([[4], [5], [6]])
... np.vstack((a,b))
array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.hstack scipy.integrate._bvp.solve_bvp numpy.ma.extras.dstack numpy.column_stack numpy.ma.extras.hstack numpy.block skimage.measure.profile.profile_line numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.split numpy.dstack numpy.ma.extras.column_stack dask.array.routines.vstack

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