numpy_transpose.Rmd

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$\newcommand{L}[1]{\| #1 \|}\newcommand{VL}[1]{\L{ \vec{#1} }}\newcommand{R}[1]{\operatorname{Re}\,(#1)}\newcommand{I}[1]{\operatorname{Im}\, (#1)}$

## `numpy.tranpose` for swapping axes

Numpy allows you to swap axes without costing anything in memory, and very
little in time.

The obvious axis swap is a 2D array transpose:

```{python}
import numpy as np
arr = np.reshape(np.arange(10), (5, 2))
arr
```

```{python}
arr.T
```

The `transpose` method - and the `np.tranpose` function does the same
thing as the `.T` attribute above:

```{python}
arr.transpose()
```

The advantage of `transpose` over the `.T` attribute is that is allows you
to move axes into any arbitrary order.

For example, let’s say you had a 3D array:

```{python}
arr = np.reshape(np.arange(24), (2, 3, 4))
arr
```

```{python}
arr.shape
```

```{python}
arr[:, :, 0]
```

`transpose` allows you to re-order these axes as you like. For example,
maybe you wanted to take the current last axis, and make it the first axis.
You pass `transpose` the order of the axes that you want:

```{python}
new_arr = arr.transpose(2, 0, 1)
```

```{python}
new_arr
```

```{python}
new_arr.shape
```

```{python}
new_arr[0, :, :]
```

Notice that the contents of the axis has not changed, just the position.
`new_arr[i, :, :]` is the same as `arr[:, :, i]` for any `i`.

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