Lee - 1 year ago 211

Python Question

For a long time, I always use

`np.array`

`np.asarray`

`np.asanyarray`

But when converting a scalar to numpy array, I know

`np.atleast_1d(123)`

`array([123])`

But I'm confused about the output of

`np.array`

`np.asarray`

`i = 123`

x = np.array(i, dtype=np.int)

print x # array(123)

print x.shape # ()

print x.size # 0

Since

`x.shape`

`x`

`array(123)`

`123`

`__str__`

A real empty array of

`size=0`

`array([])`

`print np.array([]).nbytes # 0`

print np.array(123).nbytes # 8

print type(np.array(123)) # numpy.ndarray

Apparently they are different, though the size of them is both

`0`

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Answer Source

I see this `0d`

case as a natural continuation of `nd`

. MATLAB makes 2d the lower bound. `numpy`

could have used `1d`

, but instead chose `0d`

.

An array consists of a data buffer, whether the value bytes are stored, a dtype (how to interpret those bytes), and `shape`

(plus `strides`

). `shape`

is (displayed as) a tuple. Python allows tuples to have 0, 1, 2 or more elements, so why shouldn't shape have the same flexibility?

Look at what `atleast_1d`

does

```
res = []
for ary in arys:
ary = asanyarray(ary)
if len(ary.shape) == 0:
result = ary.reshape(1)
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
```

It can work with a list of inputs (scalar, array,list etc)

```
In [374]: np.atleast_1d(np.array(1),np.array([1]),np.array([[1]]))
Out[374]: [array([1]), array([1]), array([[1]])]
```

It converts each to array (as needed) and then checks the dim (len of shape). If 0d it reshapes it to (1,). This reshape does not change the data buffer. `atleast_2d`

does `result = ary.reshape(1, 1)`

.

You could also `ndmin`

:

```
In [382]: np.array(1,ndmin=1)
Out[382]: array([1])
```

`np.array(1)`

is in many ways like `np.int32(1)`

. Both have `()`

shape, both have methods like `sum()`

. The only obvious difference is in their print format.

I don't know of any reason to purposefully construct a 0d array. It's just as easy to write `np.array([1])`

if I really want a 1d array. But you should know how to handle one if it comes up. That includes using `.item()`

to extract the scalar value, and indexing with `[()]`

.

I've encountered it most often in SO questions about loading MATLAB files with `scipy.io.loadmat`

. Some `MATLAB`

constructs are returned as 0d object arrays.

Another way of thinking about a 0d array is that it adds (or retains) the whole suit of array methods to a scalar - without having to explicitly specify the `dtype`

.

I mentioned the similarity to `np.int32(1)`

. I've seen it in beginner's code, but have not needed it myself.

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