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# Stack and Split
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<nav markdown="1" class="toc-class">
* TOC
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</nav>
## The goal
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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This is an optional topic!
## [numpy.column_stack](https://numpy.org/doc/stable/reference/generated/numpy.column_stack.html)
```python
numpy.column_stack(tup)
```
> Stack 1-D arrays as columns into a 2-D array.
>
> Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.
```python
import numpy as np
a = np.arange(0, 10)
print(a) # -> [0 1 2 3 4 5 6 7 8 9]
print(a.shape) # -> (10,)
b = np.column_stack((a, a))
print(b)
print(b.shape) # -> (10, 2)
```
Output:
```python
[[0 0]
[1 1]
[2 2]
[3 3]
[4 4]
[5 5]
[6 6]
[7 7]
[8 8]
[9 9]]
```
## [numpy.row_stack](https://numpy.org/doc/stable/reference/generated/numpy.row_stack.html)
```python
numpy.row_stack(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.
>
> np.row_stack is an alias for vstack. They are the same function.
```python
import numpy as np
a = np.arange(0, 10)
print(a) # -> [0 1 2 3 4 5 6 7 8 9]
print(a.shape) # -> (10,)
b = np.row_stack((a, a))
print(b)
print(b.shape) # -> (2, 10)
```
Output:
```python
[[0 1 2 3 4 5 6 7 8 9]
[0 1 2 3 4 5 6 7 8 9]]
```