Alex - 1 year ago 120

Python Question

I am trying the following:

`rands = np.empty((0, 10))`

rand = np.random.normal(1, 0.1, 10)

rands = np.concatenate((rands,rand),axis=0)

which gives me the following error:

`ValueError: all the input arrays must have same number of dimensions`

But why is this error? Why can't I append a new row

`rand`

`rands`

I can 'fix' this by using the following command:

`rands = np.concatenate((rands,rand.reshape(1, 10)),axis=0)`

but it looks not pythonic anymore, but cumbersome...

Maybe there is a better solution with less brackets and reshaping...?

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Answer Source

`rands`

has shape `(0, 10)`

and `rand`

has shape `(10,)`

.

```
In [19]: rands.shape
Out[19]: (0, 10)
In [20]: rand.shape
Out[20]: (10,)
```

If you try to concatenate along the 0-axis, then the 0-axis of `rands`

(of length 0) is concatenated with the 0-axis of `rand`

(of length 10).
Pictorially, it looks like this:

rands:

```
| | | | | | | | | | |
```

rand:

```
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
```

The two shapes do not fit together well because the 1-axis of `rands`

has length 10 and `rand`

lacks a 1-axis.

To fix the problem, you could promote `rand`

to a 2D array of shape `(1, 10)`

:

```
In [21]: rand[None,:].shape
Out[21]: (1, 10)
```

So that the 10 items in `rand`

are now laid out along the 1-axis. Then

```
rands = np.concatenate((rands,rand[None,:]), axis=0)
```

returns an array of shape `(1, 10)`

```
In [26]: np.concatenate((rands,rand[None,:]),axis=0).shape
Out[26]: (1, 10)
```

Alternatively, you could use `row_stack`

without promoting `rand`

to a 2D array:

```
In [28]: np.row_stack((rands,rand)).shape
Out[28]: (1, 10)
```

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