numpy.concatenate¶

numpy.concatenate((a1, a2, ...), axis=0)

Join a sequence of arrays together.

Parameters : a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. res : ndarray The concatenated array.

ma.concatenate
Concatenate function that preserves input masks.
array_split
Split an array into multiple sub-arrays of equal or near-equal size.
split
Split array into a list of multiple sub-arrays of equal size.
hsplit
Split array into multiple sub-arrays horizontally (column wise)
vsplit
Split array into multiple sub-arrays vertically (row wise)
dsplit
Split array into multiple sub-arrays along the 3rd axis (depth).
hstack
Stack arrays in sequence horizontally (column wise)
vstack
Stack arrays in sequence vertically (row wise)
dstack
Stack arrays in sequence depth wise (along third dimension)

Notes

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.

Examples

```>>> 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]])
```

```>>> a = np.ma.arange(3)
>>> b = np.arange(2, 5)
>>> a
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
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)
```

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numpy.column_stack

numpy.dstack