Function vstack

numpy/_core/shape_base.py:219–290

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

(tup, *, dtype=None, casting="same_kind")

@array_function_dispatch(_vhstack_dispatcher)
def vstack(tup, *, dtype=None, casting="same_kind"):
    """
    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. In the case of a single
        array_like input, it will be treated as a sequence of arrays; i.e.,
        each element along the zeroth axis is treated as a separate array.

    dtype : str or dtype
        If provided, the destination array will have this dtype. Cannot be
        provided together with `out`.

        .. versionadded:: 1.24

    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        Controls what kind of data casting may occur. Defaults to 'same_kind'.

        .. versionadded:: 1.24

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

    See Also
    --------
    concatenate : Join a sequence of arrays along an existing axis.
    stack : Join a sequence of arrays along a new axis.
    block : Assemble an nd-array from nested lists of blocks.
    hstack : Stack arrays in sequence horizontally (column wise).
    dstack : Stack arrays in sequence depth wise (along third axis).
    column_stack : Stack 1-D arrays as columns into a 2-D array.
    vsplit : Split an array into multiple sub-arrays vertically (row-wise).
    unstack : Split an array into a tuple of sub-arrays along an axis.

    Examples
    --------
    >>> import numpy as np
    >>> 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]])