concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")
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.
The arrays must have the same shape, except in the dimension corresponding to :None:None:`axis` (the first, by default).
The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.
If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.
If provided, the destination array will have this dtype. Cannot be provided together with :None:None:`out`.
Controls what kind of data casting may occur. Defaults to 'same_kind'.
The concatenated array.
Join a sequence of arrays along an existing axis.
array_split
Split an array into multiple sub-arrays of equal or near-equal size.
block
Assemble arrays from blocks.
column_stack
Stack 1-D arrays as columns into a 2-D array.
dsplit
Split array into multiple sub-arrays along the 3rd axis (depth).
dstack
Stack arrays in sequence depth wise (along third dimension).
hsplit
Split array into multiple sub-arrays horizontally (column wise).
hstack
Stack arrays in sequence horizontally (column wise).
ma.concatenate
Concatenate function that preserves input masks.
split
Split array into a list of multiple sub-arrays of equal size.
stack
Stack a sequence of arrays along a new axis.
vsplit
Split array into multiple sub-arrays vertically (row wise).
vstack
Stack arrays in sequence vertically (row wise).
>>> 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]])
>>> np.concatenate((a, b), axis=None) array([1, 2, 3, 4, 5, 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)See :
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