I wonder what the best way of normalizing/standardizing a numpy `recarray` is.
To make it clear, I'm not talking about a mathematical matrix, but a record array that also has e.g. textual columns (such as labels).

```
a = np.genfromtxt("iris.csv", delimiter=",", dtype=None)
print a.shape
> (150,)

```

As you can see, I cannot e.g. process `a[:,:-1]` as the shape is one-dimensional.

The best I found is to iterate over all columns:

```
for nam in a.dtype.names[:-1]:
    col = a[nam]
    a[nam] = (col - col.min()) / (col.max() - col.min())

```

Any more elegant way of doing this? Is there some method such as "normalize" or "standardize" somewhere?