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Is there a method analogous to `numpy.split`

that returns a `numpy.ndarray`

instead of a `list`

? Assuming that the array splits evenly is fine (to prevent jagged arrays).

For example:

```
x = np.arange(9.0)
print(np.split(x, 3))
# [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
print(np.???(x, 3))
# array([[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]])
```

I’d rather not stack the list given from `np.split`

together for performance reasons.

### >Solution :

Sounds like you just want to reshape the array:

```
numpy.reshape(x, (3, -1))
```

Here, `(3, -1)`

means 3 rows and an inferred number of columns.