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numpy.ma.row_stack

numpy.ma.append

# numpy.ma.vstack¶

numpy.ma.vstack(tup) = <numpy.ma.extras._fromnxfunction_seq object>

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (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. stacked : ndarray The array formed by stacking the given arrays, will be at least 2-D.

stack
Join a sequence of arrays along a new axis.
hstack
Stack arrays in sequence horizontally (column wise).
dstack
Stack arrays in sequence depth wise (along third dimension).
concatenate
Join a sequence of arrays along an existing axis.
vsplit
Split array into a list of multiple sub-arrays vertically.
block
Assemble arrays from blocks.

Notes

The function is applied to both the _data and the _mask, if any.

Examples

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