Amelio Vazquez-Reina - 4 months ago 17

Python Question

Say I have an array

`a`

`a = np.array([[1,2,3], [4,5,6]])`

array([[1, 2, 3],

[4, 5, 6]])

I would like to convert it to a 1D array (i.e. a column vector):

`b = np.reshape(a, (1,np.product(a.shape)))`

but this returns

`array([[1, 2, 3, 4, 5, 6]])`

which is not the same as:

`array([1, 2, 3, 4, 5, 6])`

I can take the first element of this array to manually convert it to a 1D array:

`b = np.reshape(a, (1,np.product(a.shape)))[0]`

but this requires me to know how many dimensions the original array has (and concatenate [0]'s when working with higher dimensions)

Is there a dimensions-independent way of getting a column/row vector from an arbitrary ndarray?

Answer

Use np.ravel (for a 1D view) or np.flatten (for a 1D copy) or np.flat (for an 1D iterator):

```
In [12]: a = np.array([[1,2,3], [4,5,6]])
In [13]: b = a.ravel()
In [14]: b
Out[14]: array([1, 2, 3, 4, 5, 6])
```

Note that `ravel()`

returns a `view`

of `a`

when possible. So modifying `b`

also modifies `a`

. `ravel()`

returns a `view`

when the 1D elements are contiguous in memory, but would return a `copy`

if, for example, `a`

were made from slicing another array using a non-unit step size (e.g. `a = x[::2]`

).

If you want a copy rather than a view, use

```
In [15]: c = a.flatten()
```

If you just want an iterator, use `np.flat`

:

```
In [20]: d = a.flat
In [21]: d
Out[21]: <numpy.flatiter object at 0x8ec2068>
In [22]: list(d)
Out[22]: [1, 2, 3, 4, 5, 6]
```